diff --git a/src/popcast/fit.py b/src/popcast/fit.py
index fee9c0e..b1b05a5 100644
--- a/src/popcast/fit.py
+++ b/src/popcast/fit.py
@@ -421,6 +421,11 @@ def _fit(self, coefficients, X, y, use_l1_penalty=True, calculate_optimal_distan
         # Estimate target values.
         y_hat = self.predict(X, coefficients)
 
+        # Save optimal frequencies by timepoint and strain, if calculating
+        # optimal distance to the future.
+        if calculate_optimal_distance:
+            optimal_frequency_records = []
+
         # Calculate EMD for each timepoint in the estimated values and sum that
         # distance across all timepoints.
         error = 0.0
@@ -471,6 +476,12 @@ def _fit(self, coefficients, X, y, use_l1_penalty=True, calculate_optimal_distan
                     cost=distance_matrix
                 )
 
+                optimal_frequency_records.append(pd.DataFrame({
+                    "strain": samples_a,
+                    "timepoint": timepoint,
+                    "optimal_projected_frequency": estimated_frequencies,
+                }))
+
             # Estimate the distance between the model's estimated future and the
             # observed future populations.
             model_emd, _, self.model_flow = cv2.EMD(
@@ -490,6 +501,9 @@ def _fit(self, coefficients, X, y, use_l1_penalty=True, calculate_optimal_distan
         else:
             l1_penalty = 0.0
 
+        if calculate_optimal_distance:
+            self.optimal_frequencies = pd.concat(optimal_frequency_records)
+
         return error + l1_penalty
 
     def _fit_distance(self, coefficients, X, y, use_l1_penalty=True):
@@ -861,6 +875,11 @@ def test(model_class, model_kwargs, data, targets, timepoints, coefficients=None
 
         # Get the estimated frequencies for test sets to export.
         test_y_hat = model.predict(test_X)
+        test_y_hat = test_y_hat.merge(
+            model.optimal_frequencies,
+            on=["timepoint", "strain"],
+            validate="1:1",
+        )
 
         # Convert timestamps to a serializable format.
         for df in [test_X, test_y, test_y_hat]: