diff --git a/searcharray/indexing.py b/searcharray/indexing.py
index 5042927..1f78c35 100644
--- a/searcharray/indexing.py
+++ b/searcharray/indexing.py
@@ -46,7 +46,7 @@ def _compute_doc_lens(posns: np.ndarray, doc_ids: np.ndarray, num_docs: int) ->
     non_empty_doc_ids = doc_ids[non_empty_idxs]
     non_empty_doc_lens = non_empty_doc_lens[non_empty_idxs]
     doc_lens[non_empty_doc_ids] = non_empty_doc_lens
-    if doc_ids[-1] not in non_empty_doc_ids:
+    if len(doc_ids) > 0 and doc_ids[-1] not in non_empty_doc_ids:
         doc_lens[doc_ids[-1]] = posns[-1] + 1
     return doc_lens
 
diff --git a/searcharray/phrase/middle_out.py b/searcharray/phrase/middle_out.py
index 5ea6fd0..434575b 100644
--- a/searcharray/phrase/middle_out.py
+++ b/searcharray/phrase/middle_out.py
@@ -170,6 +170,8 @@ def build(self):
         encoded, enc_term_boundaries = encoder.encode(keys=self.flat_array[1].view(np.uint64),
                                                       boundaries=term_boundaries[:-1],
                                                       payload=self.flat_array[2].view(np.uint64))
+        if len(encoded) == 0:
+            return PosnBitArray({}, self.max_doc_id)
         term_ids = self.flat_array[0][term_boundaries[:-1]]
 
         encoded_term_posns = ArrayDict.from_array_with_boundaries(encoded,
diff --git a/searcharray/similarity.py b/searcharray/similarity.py
index 7f85b5d..d096df9 100644
--- a/searcharray/similarity.py
+++ b/searcharray/similarity.py
@@ -43,7 +43,10 @@ def compute_idf(num_docs, dfs):
 
 
 def compute_adj_doc_lens(doc_lens, avg_doc_lens, k1, b):
-    adj_doc_lens = doc_lens / avg_doc_lens
+    if avg_doc_lens == 0:
+        adj_doc_lens = np.zeros_like(doc_lens, dtype=np.float32)
+    else:
+        adj_doc_lens = doc_lens / avg_doc_lens
     adj_doc_lens *= b
     adj_doc_lens += 1 - b
     adj_doc_lens *= k1
diff --git a/test/test_search.py b/test/test_search.py
index c1340a3..6786729 100644
--- a/test/test_search.py
+++ b/test/test_search.py
@@ -1,5 +1,6 @@
 """Test postings array search functionality."""
 import numpy as np
+import pandas as pd
 import pytest
 from searcharray.postings import SearchArray
 from searcharray.similarity import bm25_similarity
@@ -12,6 +13,16 @@ def data():
     return SearchArray.index(["foo bar bar baz", "data2", "data3 bar", "bunny funny wunny"] * 25)
 
 
+@pytest.fixture
+def all_empty_str():
+    return pd.DataFrame({"data": [""] * 100})
+
+
+def test_search_empty_str(all_empty_str):
+    data = SearchArray.index(all_empty_str["data"])
+    assert data.score("foo").sum() == 0
+
+
 def test_match(data):
     matches = data.termfreqs("foo") > 0
     assert (matches == [True, False, False, False] * 25).all()