diff --git a/pyterrier_dr/_mmr.py b/pyterrier_dr/_mmr.py
index 5931497..b9526cb 100644
--- a/pyterrier_dr/_mmr.py
+++ b/pyterrier_dr/_mmr.py
@@ -12,17 +12,19 @@ class MmrScorer(pt.Transformer):
 
     .. cite.dblp:: conf/sigir/CarbonellG98
     """
-    def __init__(self, *, Lambda: float = 0.5, norm_rel: bool = False, norm_sim: bool = False, verbose: bool = False):
+    def __init__(self, *, Lambda: float = 0.5, norm_rel: bool = False, norm_sim: bool = False, drop_doc_vec: bool = True, verbose: bool = False):
         """
         Args:
             Lambda: The balance parameter between relevance and diversity (default: 0.5)
             norm_rel: Whether to normalize relevance scores to [0, 1] (default: False)
             norm_sim: Whether to normalize similarity scores to [0, 1] (default: False)
+            drop_doc_vec: Whether to drop the 'doc_vec' column after re-ranking (default: True)
             verbose: Whether to display verbose output (e.g., progress bars) (default: False)
         """
         self.Lambda = Lambda
         self.norm_rel = norm_rel
         self.norm_sim = norm_sim
+        self.drop_doc_vec = drop_doc_vec
         self.verbose = verbose
 
     def transform(self, inp: pd.DataFrame) -> pd.DataFrame:
@@ -51,10 +53,13 @@ def transform(self, inp: pd.DataFrame) -> pd.DataFrame:
                 if marg_rels.shape[0] > 1:
                     marg_rels = np.max(np.stack([marg_rels, dvec_sims[idx]]), axis=0)
                     marg_rels[idx] = float('inf') # ignore this document from now on
-            out.append(frame.iloc[new_idxs].reset_index(drop=True).assign(
+            new_frame = frame.iloc[new_idxs].reset_index(drop=True).assign(
                 score=-np.arange(len(new_idxs), dtype=float),
                 rank=np.arange(len(new_idxs))
-            ))
+            )
+            if self.drop_doc_vec:
+                new_frame = new_frame.drop(columns='doc_vec')
+            out.append(new_frame)
 
         return pd.concat(out, ignore_index=True)
 
diff --git a/pyterrier_dr/flex/diversity.py b/pyterrier_dr/flex/diversity.py
index d61bec1..66cc287 100644
--- a/pyterrier_dr/flex/diversity.py
+++ b/pyterrier_dr/flex/diversity.py
@@ -3,7 +3,7 @@
 from . import FlexIndex
 
 
-def _mmr(self, *, Lambda: float = 0.5, norm_rel: bool = False, norm_sim: bool = False, verbose: bool = False) -> pt.Transformer:
+def _mmr(self, *, Lambda: float = 0.5, norm_rel: bool = False, norm_sim: bool = False, drop_doc_vec: bool = True, verbose: bool = False) -> pt.Transformer:
     """Returns an MMR (Maximal Marginal Relevance) scorer (i.e., re-ranker) over this index.
 
     The method first loads vectors from the index and then applies :class:`MmrScorer` to re-rank the results. See
@@ -13,9 +13,10 @@ def _mmr(self, *, Lambda: float = 0.5, norm_rel: bool = False, norm_sim: bool =
         Lambda: The balance parameter between relevance and diversity (default: 0.5)
         norm_rel: Whether to normalize relevance scores to [0, 1] (default: False)
         norm_sim: Whether to normalize similarity scores to [0, 1] (default: False)
+        drop_doc_vec: Whether to drop the 'doc_vec' column after re-ranking (default: True)
         verbose: Whether to display verbose output (e.g., progress bars) (default: False)
 
     .. cite.dblp:: conf/sigir/CarbonellG98
     """
-    return self.vec_loader() >> pyterrier_dr.MmrScorer(Lambda=Lambda, norm_rel=norm_rel, norm_sim=norm_sim, verbose=verbose)
+    return self.vec_loader() >> pyterrier_dr.MmrScorer(Lambda=Lambda, norm_rel=norm_rel, norm_sim=norm_sim, drop_doc_vec=drop_doc_vec, verbose=verbose)
 FlexIndex.mmr = _mmr
diff --git a/tests/test_mmr.py b/tests/test_mmr.py
new file mode 100644
index 0000000..2d3cd9b
--- /dev/null
+++ b/tests/test_mmr.py
@@ -0,0 +1,31 @@
+import unittest
+import numpy as np
+import pandas as pd
+from pyterrier_dr import MmrScorer
+
+
+class TestMmr(unittest.TestCase):
+    def test_mmr(self):
+        mmr = MmrScorer()
+        results = mmr(pd.DataFrame([
+            ['q0', 'd0', 1.0, np.array([0, 1, 0])],
+            ['q0', 'd1', 0.5, np.array([0, 1, 1])],
+            ['q0', 'd2', 0.5, np.array([1, 1, 1])],
+            ['q0', 'd3', 0.1, np.array([1, 1, 0])],
+            ['q1', 'd0', 0.6, np.array([0, 1, 0])],
+            ['q2', 'd0', 0.4, np.array([0, 1, 0])],
+            ['q2', 'd1', 0.3, np.array([0, 1, 1])],
+        ], columns=['qid', 'docno', 'score', 'doc_vec']))
+        pd.testing.assert_frame_equal(results, pd.DataFrame([
+            ['q0', 'd0', 0.0, 0],
+            ['q0', 'd2', -1.0, 1],
+            ['q0', 'd1', -2.0, 2],
+            ['q0', 'd3', -3.0, 3],
+            ['q1', 'd0', 0.0, 0],
+            ['q2', 'd0', 0.0, 0],
+            ['q2', 'd1', -1.0, 1],
+        ], columns=['qid', 'docno', 'score', 'rank']))
+
+
+if __name__ == '__main__':
+    unittest.main()