diff --git a/ann_benchmarks/.DS_Store b/ann_benchmarks/.DS_Store
index f4d2c77..e7f84d2 100644
Binary files a/ann_benchmarks/.DS_Store and b/ann_benchmarks/.DS_Store differ
diff --git a/ann_benchmarks/algorithms/chemfp.py b/ann_benchmarks/algorithms/chemfp.py
index 3a6f29e..3e8a7c2 100644
--- a/ann_benchmarks/algorithms/chemfp.py
+++ b/ann_benchmarks/algorithms/chemfp.py
@@ -15,14 +15,14 @@ def __init__(self, metric):
         self.name = "Chemfp()"
 
     @staticmethod
-    def matrToArena(X):
+    def matrToArena(X, reorder=True):
         # convert X to Chemfp fingerprintArena in memory
         fps = []
         for row in range(X.shape[0]):
             fp = bitarray(endian='big')
             fp.extend(X[row])
             fps.append((row,fp.tobytes()))
-        return chemfp.load_fingerprints(fps,chemfp.Metadata(num_bits=X.shape[1]))
+        return chemfp.load_fingerprints(fps,chemfp.Metadata(num_bits=X.shape[1]),reorder=reorder)
 
     def pre_fit(self, X):
         self._target = Chemfp.matrToArena(X)
@@ -45,3 +45,19 @@ def post_query(self, rq=False):
                 return hits.get_ids()
             else:
                 return []
+    def pre_batch_query(self, X, n):
+        self._queries = Chemfp.matrToArena(X, False)
+
+    def batch_query(self, X, n):
+        self._results = chemfp.knearest_tanimoto_search(self._queries, self._target, k=n, threshold=0.0)
+
+    def get_batch_results(self):
+        # parse the results
+        res = []
+        for (query_id, hits) in self._results:
+            if hits:
+                res.append(hits.get_ids())
+            else:
+                res.append([])
+        print(res)
+        return res
diff --git a/ann_benchmarks/runner.py b/ann_benchmarks/runner.py
index 9acaa4d..fce0723 100644
--- a/ann_benchmarks/runner.py
+++ b/ann_benchmarks/runner.py
@@ -63,6 +63,9 @@ def single_query(v):
             return (total, candidates)
 
         def batch_query(X):
+            # special code for Chemfp
+            if algoname in ['Chemfp']:
+                algo.pre_batch_query(X, count)
             start = time.time()
             algo.batch_query(X, count)
             total = (time.time() - start)