diff --git a/pyserini/search/faiss/_searcher.py b/pyserini/search/faiss/_searcher.py
index 23a024deb..c71992371 100644
--- a/pyserini/search/faiss/_searcher.py
+++ b/pyserini/search/faiss/_searcher.py
@@ -439,7 +439,7 @@ def list_prebuilt_indexes():
         """Display information about available prebuilt indexes."""
         get_dense_indexes_info()
 
-    def search(self, query: Union[str, np.ndarray], k: int = 10, threads: int = 1, return_vector: bool = False) \
+    def search(self, query: Union[str, np.ndarray], k: int = 10, threads: int = 1, remove_dups: bool = False, return_vector: bool = False) \
             -> Union[List[DenseSearchResult], Tuple[np.ndarray, List[PRFDenseSearchResult]]]:
         """Search the collection.
 
@@ -451,6 +451,8 @@ def search(self, query: Union[str, np.ndarray], k: int = 10, threads: int = 1, r
             Number of hits to return.
         threads : int
             Maximum number of threads to use for intra-query search.
+        remove_dups : bool
+            Remove duplicate docids when writing final run output.    
         return_vector : bool
             Return the results with vectors
         Returns
@@ -477,6 +479,14 @@ def search(self, query: Union[str, np.ndarray], k: int = 10, threads: int = 1, r
             distances, indexes = self.index.search(emb_q, k)
             distances = distances.flat
             indexes = indexes.flat
+            if remove_dups:
+                unique_docs = set()
+                results = list()
+                for score, idx in zip(distances, indexes):
+                    if idx not in unique_docs:
+                        unique_docs.add(idx)
+                        results.append(DenseSearchResult(self.docids[idx],score))
+                return results
             return [DenseSearchResult(self.docids[idx], score)
                     for score, idx in zip(distances, indexes) if idx != -1]