diff --git a/Taskfile.yaml b/Taskfile.yaml
index eb8feac92..f62d353df 100644
--- a/Taskfile.yaml
+++ b/Taskfile.yaml
@@ -53,6 +53,7 @@ tasks:
       OBJC_DISABLE_INITIALIZE_FORK_SAFETY: "YES"
       RESULTS_DIR: ../../docs/pages/performance/fashion-mnist
     cmds:
+#      - venv/bin/pip install ../../client-python
       - venv/bin/python run.py --dataset fashion-mnist-784-euclidean --algorithm elastiknn-l2lsh --runs 3 --count 100 --parallelism 1 --force --local
       - mkdir -p $RESULTS_DIR
       - venv/bin/python plot.py --dataset fashion-mnist-784-euclidean --count 100 --output $RESULTS_DIR/plot.png | venv/bin/python ../parse_results.py > $RESULTS_DIR/results.md
diff --git a/client-python/elastiknn/api.py b/client-python/elastiknn/api.py
index 4a5c3671b..684078945 100644
--- a/client-python/elastiknn/api.py
+++ b/client-python/elastiknn/api.py
@@ -51,6 +51,11 @@ def to_dict(self):
         def __len__(self):
             return len(self.values)
 
+        @staticmethod
+        def random(length: int, rng: Random = Random(time())):
+            values = [rng.random() for _ in range(length)]
+            return Vec.DenseFloat(values)
+
     @dataclass(frozen=True)
     class Indexed(Base):
         index: str
diff --git a/client-python/elastiknn/client.py b/client-python/elastiknn/client.py
index 9c4fd1a79..fae55ad02 100644
--- a/client-python/elastiknn/client.py
+++ b/client-python/elastiknn/client.py
@@ -2,6 +2,7 @@
 
 from typing import Iterable, Tuple, Dict, Optional
 
+import numpy as np
 from elasticsearch import Elasticsearch
 from elasticsearch.helpers import bulk
 
@@ -28,7 +29,38 @@ def __init__(self, es: Elasticsearch = None):
         else:
             self.es = es
 
-    def put_mapping(self, index: str, vec_field: str, mapping: Mapping.Base, stored_id_field: str):
+    def create_index(self,
+                     index: str,
+                     vec_field: str,
+                     mapping: Mapping.Base,
+                     stored_id_field: str,
+                     sort_by_distance_field: str = None):
+        properties = {
+            vec_field: mapping.to_dict(),
+            stored_id_field: {
+                "type": "keyword",
+                "store": True
+            }
+        }
+        if sort_by_distance_field is not None:
+            properties[sort_by_distance_field] = {"type": "double"}
+        mapping = {"properties": properties}
+
+        settings = {}
+        if sort_by_distance_field is not None:
+            properties[sort_by_distance_field] = {"type": "double"}
+            settings["index"] = {
+                "sort.field": [sort_by_distance_field],
+                "sort.order": ["asc"]
+            }
+
+        self.es.indices.create(index=index, mappings=mapping, settings=settings)
+
+    def put_mapping(self,
+                    index: str,
+                    vec_field: str,
+                    mapping: Mapping.Base,
+                    stored_id_field: str):
         """
         Update the mapping at the given index and field to store an Elastiknn vector.
 
@@ -57,7 +89,15 @@ def put_mapping(self, index: str, vec_field: str, mapping: Mapping.Base, stored_
         }
         return self.es.indices.put_mapping(properties=properties, index=index)
 
-    def index(self, index: str, vec_field: str, vecs: Iterable[Vec.Base], stored_id_field: str, ids: Iterable[str], refresh: bool = False) -> Tuple[int, List[Dict]]:
+    def index(self,
+              index: str,
+              vec_field: str,
+              vecs: Iterable[Vec.Base],
+              stored_id_field: str,
+              ids: Iterable[str],
+              refresh: bool = False,
+              sort_by_distance_field: str = None
+              ) -> Tuple[int, List[Dict]]:
         """Index (i.e. store) the given vectors at the given index and field with the optional ids.
 
         Parameters
@@ -74,7 +114,10 @@ def index(self, index: str, vec_field: str, vecs: Iterable[Vec.Base], stored_id_
             Field containing the document ID. Uses `store: true` setting as an optimization for faster id-only queries.
         refresh : bool
             Whether to refresh before returning. Set to true if you want to immediately run queries after indexing.
-
+        sort_by_distance_field:
+            Field containing the vector's distance from the origin vector (all zeros).
+            Used as an optimization to co-locate vectors that are close together.
+            If None, we don't store this value.
         Returns
         -------
         Int
@@ -85,7 +128,19 @@ def index(self, index: str, vec_field: str, vecs: Iterable[Vec.Base], stored_id_
 
         def gen():
             for vec, _id in zip(vecs, ids):
-                yield { "_op_type": "index", "_index": index, vec_field: vec.to_dict(), stored_id_field: str(_id), "_id": str(_id) }
+                doc = {
+                    "_op_type": "index",
+                    "_index": index,
+                    vec_field: vec.to_dict(),
+                    stored_id_field: str(_id),
+                    "_id": str(_id)
+                }
+                if sort_by_distance_field is not None and isinstance(vec, Vec.DenseFloat):
+                    zeros = np.zeros(len(vec.values))
+                    npvec = np.array(vec.values)
+                    l2dst = np.sqrt(np.sum((zeros - npvec) ** 2))
+                    doc[sort_by_distance_field] = l2dst
+                yield doc
 
         res = bulk(self.es, gen(), chunk_size=200, max_retries=9)
         if refresh:
diff --git a/client-python/elastiknn/models.py b/client-python/elastiknn/models.py
index 03291076e..d20ce3bc7 100644
--- a/client-python/elastiknn/models.py
+++ b/client-python/elastiknn/models.py
@@ -22,6 +22,7 @@ def __init__(self, algorithm: str, metric: str, es: Elasticsearch = None, mappin
         self._logger = Logger(self.__class__.__name__)
         self._vec_field = "vec"
         self._stored_id_field = "id"
+        self._sort_by_distance_field = "vec_distance_from_origin"
         self._index = index
         self._mapping_params = mapping_params
         self._query_params = query_params
@@ -43,15 +44,35 @@ def fit(self, X: Union[np.ndarray, csr_matrix, List[Vec.SparseBool], List[Vec.De
             self._index = f"{ELASTIKNN_NAME}-{int(time())}"
             self._logger.warning(f"index was not given, using {self._index} instead")
 
+        settings = {
+            "index": {
+                "number_of_shards": shards,
+                "number_of_replicas": 0,
+                "sort.field": [self._sort_by_distance_field],
+                "sort.order": ["asc"]
+            }
+        }
+        mappings = {
+            "properties": {
+                self._vec_field: mapping.to_dict(),
+                self._stored_id_field: {
+                    "type": "keyword",
+                    "store": True
+                },
+                self._sort_by_distance_field: {
+                    "type": "double"
+                }
+            }
+        }
+
         self._eknn.es.indices.delete(index=self._index, ignore_unavailable=True)
-        self._eknn.es.indices.create(index=self._index, settings=dict(number_of_shards=shards, elastiknn=True, number_of_replicas=0))
-        self._eknn.put_mapping(self._index, self._vec_field, mapping, self._stored_id_field)
+        self._eknn.es.indices.create(index=self._index, settings=settings, mappings=mappings)
 
         self._logger.info(f"indexing {len(X)} vectors into index {self._index}")
         ids = map(lambda i: str(i + 1), range(len(X)))  # Add one because 0 is an invalid id in ES.
-        self._eknn.index(self._index, self._vec_field, vecs, self._stored_id_field, ids, refresh=True)
+        self._eknn.index(self._index, self._vec_field, vecs, self._stored_id_field, ids, refresh=True,
+                         sort_by_distance_field=self._sort_by_distance_field)
         self._eknn.es.indices.forcemerge(index=self._index, max_num_segments=1)
-        self._eknn.index(self._index, self._vec_field, [], self._stored_id_field, [], refresh=True)
 
 
     def set_query_params(self, query_params: dict = None):
diff --git a/client-python/tests/test_client.py b/client-python/tests/test_client.py
index 4b57b670f..75e1a1247 100644
--- a/client-python/tests/test_client.py
+++ b/client-python/tests/test_client.py
@@ -35,3 +35,34 @@ def test_exact_jaccard(self):
         hits = res['hits']['hits']
         assert len(hits) == 10
         assert hits[0]["_id"] == ids[0]
+
+    def test_sorting(self):
+        eknn = ElastiknnClient()
+        n, dim = 1200, 42
+        index = "py-test-sorting"
+        vec_field = "vec"
+        id_field = "id"
+        sort_by_distance_field = "vec_distance_from_origin"
+        mapping = Mapping.DenseFloat(dims=dim)
+
+        eknn.es.indices.delete(index=index, ignore_unavailable=True)
+        eknn.es.indices.refresh()
+        eknn.create_index(index, vec_field, mapping, id_field, sort_by_distance_field)
+        eknn.es.indices.refresh()
+
+        vecs = [Vec.DenseFloat.random(dim) for _ in range(n)]
+        ids = [f"vec-{i}" for i in range(n)]
+        (n_, errors) = eknn.index(index, vec_field, vecs, id_field, ids, refresh=True,
+                                  sort_by_distance_field=sort_by_distance_field)
+        assert n_ == n
+        assert len(errors) == 0
+
+        settings = eknn.es.indices.get_settings(index=index)
+        sort_settings = settings.body[index]["settings"]["index"]["sort"]
+        assert sort_settings["field"] == [sort_by_distance_field]
+        assert sort_settings["order"] == ["asc"]
+
+        results = eknn.es.search(index=index)
+        for hit in results.body["hits"]["hits"]:
+            src = hit["_source"]
+            assert src[sort_by_distance_field] > 0, src
diff --git a/docs/pages/performance/fashion-mnist/plot.b64 b/docs/pages/performance/fashion-mnist/plot.b64
index c8562d6d3..2d7f80a71 100644
--- a/docs/pages/performance/fashion-mnist/plot.b64
+++ b/docs/pages/performance/fashion-mnist/plot.b64
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/docs/pages/performance/fashion-mnist/plot.png b/docs/pages/performance/fashion-mnist/plot.png
index d645724eb..3ac53fef9 100644
Binary files a/docs/pages/performance/fashion-mnist/plot.png and b/docs/pages/performance/fashion-mnist/plot.png differ
diff --git a/docs/pages/performance/fashion-mnist/results.md b/docs/pages/performance/fashion-mnist/results.md
index ec02e82bd..bbac99914 100644
--- a/docs/pages/performance/fashion-mnist/results.md
+++ b/docs/pages/performance/fashion-mnist/results.md
@@ -1,10 +1,10 @@
 |Model|Parameters|Recall|Queries per Second|
 |---|---|---|---|
-|eknn-l2lsh|L=100 k=4 w=1024 candidates=500 probes=0|0.378|337.457|
-|eknn-l2lsh|L=100 k=4 w=1024 candidates=1000 probes=0|0.446|281.828|
-|eknn-l2lsh|L=100 k=4 w=1024 candidates=500 probes=3|0.634|272.814|
-|eknn-l2lsh|L=100 k=4 w=1024 candidates=1000 probes=3|0.716|232.698|
-|eknn-l2lsh|L=100 k=4 w=2048 candidates=500 probes=0|0.767|303.686|
-|eknn-l2lsh|L=100 k=4 w=2048 candidates=1000 probes=0|0.846|254.121|
-|eknn-l2lsh|L=100 k=4 w=2048 candidates=500 probes=3|0.922|215.233|
-|eknn-l2lsh|L=100 k=4 w=2048 candidates=1000 probes=3|0.960|190.689|
+|eknn-l2lsh|L=100 k=4 w=1024 candidates=500 probes=0|0.367|336.832|
+|eknn-l2lsh|L=100 k=4 w=1024 candidates=1000 probes=0|0.412|280.243|
+|eknn-l2lsh|L=100 k=4 w=1024 candidates=500 probes=3|0.626|274.929|
+|eknn-l2lsh|L=100 k=4 w=1024 candidates=1000 probes=3|0.710|236.222|
+|eknn-l2lsh|L=100 k=4 w=2048 candidates=500 probes=0|0.763|307.143|
+|eknn-l2lsh|L=100 k=4 w=2048 candidates=1000 probes=0|0.843|263.197|
+|eknn-l2lsh|L=100 k=4 w=2048 candidates=500 probes=3|0.920|216.136|
+|eknn-l2lsh|L=100 k=4 w=2048 candidates=1000 probes=3|0.959|190.726|