diff --git a/wbtools/literature/corpus.py b/wbtools/literature/corpus.py
index 08563fb..2e719b8 100644
--- a/wbtools/literature/corpus.py
+++ b/wbtools/literature/corpus.py
@@ -174,7 +174,7 @@ def load(self, file_path: str) -> None:
     def query_papers_by_doc_similarity(self, query_docs: List[str], sentence_search: bool = False,
                                        remove_sections: List[PaperSections] = None,
                                        must_be_present: List[PaperSections] = None, path_to_model: str = None,
-                                       average_match: bool = True) -> List[SimilarityResult]:
+                                       average_match: bool = True, num_best: int = 10) -> List[SimilarityResult]:
         """query papers in the corpus by similarity with the provided query documents, which can be fulltext documents
            or sentences
 
@@ -186,6 +186,7 @@ def query_papers_by_doc_similarity(self, query_docs: List[str], sentence_search:
                                                    sections
             path_to_model (str): path to word2vec model
             average_match (bool): merge query documents and calculate average similarity to them
+            num_best (int): limit to the first n results by similarity score
 
         Returns:
             List[SimilarityResult]: list of papers most similar to the provided query documents
@@ -198,12 +199,13 @@ def query_papers_by_doc_similarity(self, query_docs: List[str], sentence_search:
             split_sentences=sentence_search, remove_sections=remove_sections, must_be_present=must_be_present,
             lowercase=False, tokenize=False, remove_stopwords=False, remove_alpha=False)
         docsim_index, dictionary = get_softcosine_index(model=model, model_path=path_to_model,
-                                                        corpus_list_token=corpus_list_token)
+                                                        corpus_list_token=corpus_list_token, num_best=num_best)
         query_docs_preprocessed = [preprocess(doc=sentence, lower=True, tokenize=True, remove_stopwords=True,
                                               remove_alpha=True) for sentence in query_docs]
         sims = get_similar_documents(docsim_index, dictionary, query_docs_preprocessed, idx_paperid_map,
                                      average_match=average_match)
-        return [SimilarityResult(score=sim.score, paper_id=sim.paper_id, match_idx=sim.match_idx,
-                                 query_idx=sim.query_idx, match="\"" + corpus_list_token_orig[sim.match_idx] + "\"",
-                                 query="\"" + (" ".join(query_docs) if average_match else query_docs[sim.query_idx]) +
-                                       "\"") for sim in sims]
+        results = [SimilarityResult(score=sim.score, paper_id=sim.paper_id, match_idx=sim.match_idx,
+                                    query_idx=sim.query_idx, match="\"" + corpus_list_token_orig[sim.match_idx] + "\"",
+                                    query="\"" + (" ".join(query_docs) if average_match else query_docs[sim.query_idx]
+                                                  ) + "\"") for sim in sims]
+        return results[0:num_best] if len(results) > num_best else results