diff --git a/README.md b/README.md
index 26e73ab62..49ff46633 100644
--- a/README.md
+++ b/README.md
@@ -254,6 +254,7 @@ With Pyserini, it's easy to replicate runs on a number of standard IR test colle
 + The easiest way, start here: [Replicating runs directly from the Python package](docs/pypi-replication.md)
 + [Guide to replicating the BM25 baseline for MS MARCO Passage Ranking](docs/experiments-msmarco-passage.md)
 + [Guide to replicating the BM25 baseline for MS MARCO Document Ranking](docs/experiments-msmarco-doc.md)
++ [Guide to replicating the multi-field BM25 baseline for MS MARCO Document Ranking from Elasticsearch](docs/experiments-elastic.md)
 + [Guide to replicating Robust04 baselines for ad hoc retrieval](docs/experiments-robust04.md)
 + [Guide to replicating TCT-ColBERT experiments for MS MARCO Passage/Document Ranking](docs/experiments-tct_colbert.md)
 + [Guide to replicating DPR experiments for Open-Domain QA](docs/experiments-dpr.md)
diff --git a/docs/experiments-elastic.md b/docs/experiments-elastic.md
index 51af93324..a00668993 100644
--- a/docs/experiments-elastic.md
+++ b/docs/experiments-elastic.md
@@ -1,17 +1,18 @@
-# Pyserini: Elastic Multi-field Baseline for MS MARCO Document Ranking
+# Pyserini: Multi-field Baseline for MS MARCO Document Ranking
 
-This page contains instructions for reproducing the Elasticsearch optimized
-multi_match best_fields" entry on the the [MS MARCO Document Ranking Leaderboard](https://microsoft.github.io/MSMARCO-Document-Ranking-Submissions/leaderboard/).
+This page contains instructions for reproducing the "Elasticsearch optimized
+multi_match best_fields" entry (2020/11/25) on the the [MS MARCO Document Ranking Leaderboard](https://microsoft.github.io/MSMARCO-Document-Ranking-Submissions/leaderboard/) using Pyserini.
+Details behind this run are described in this [blog post](https://www.elastic.co/blog/improving-search-relevance-with-data-driven-query-optimization);
+the official leaderboard submission corresponds to the run denoted "multi_match best_fields tuned (all-in-one): all
+params" in the blog post.
 
 This run makes sure to preserve the distinction between document fields when
 preparing and indexing documents. For ranking, we use a disjunction max query to
-combine score contributions across fields. The weights for the disjunction max
-query are taken from the "multi_match best_fields tuned (all-in-one): all
-params" entry in the [blog post](https://www.elastic.co/blog/improving-search-relevance-with-data-driven-query-optimization)
-that describes the leaderboard submission.
+combine score contributions across fields; the weights for the disjunction max
+query are taken from the blog post reference above.
 
 To match the leaderboard results, this run makes use of a custom stopwords file
-'elastic-msmarco-stopwords.txt'. The file contains the default English stopwords
+[`elastic-msmarco-stopwords.txt`](elastic-msmarco-stopwords.txt). The file contains the default English stopwords
 from Lucene, plus some additional words targeted at question-style queries.
 
 ## Data Prep
@@ -69,9 +70,7 @@ python -m pyserini.search --msmarco --hits 100 \
 After the run completes, we can evaluate the results:
 
 ```bash
-python tools/scripts/msmarco/msmarco_doc_eval.py \
-  --judgments tools/topics-and-qrels/qrels.msmarco-doc.dev.txt \
-  --run runs/run.msmarco-doc.leaderboard-dev.elastic.txt
+$ python -m pyserini.eval.msmarco_doc_eval --judgments msmarco-doc-dev --run runs/run.msmarco-doc.leaderboard-dev.elastic.txt
 #####################
 MRR @100: 0.3071421845448626
 QueriesRanked: 5193
diff --git a/pyserini/search/_searcher.py b/pyserini/search/_searcher.py
index c0f255ea7..70afb7da0 100644
--- a/pyserini/search/_searcher.py
+++ b/pyserini/search/_searcher.py
@@ -111,9 +111,9 @@ def search(self, q: Union[str, JQuery], k: int = 10, query_generator: JQueryGene
         hits = None
         if query_generator:
             if not fields:
-                hits = self.object.search(query_generator, JString(q), k)
+                hits = self.object.search(query_generator, JString(q.encode('utf8')), k)
             else:
-                hits = self.object.searchFields(query_generator, JString(q), jfields, k)
+                hits = self.object.searchFields(query_generator, JString(q.encode('utf8')), jfields, k)
         elif isinstance(q, JQuery):
             # Note that RM3 requires the notion of a query (string) to estimate the appropriate models. If we're just
             # given a Lucene query, it's unclear what the "query" is for this estimation. One possibility is to extract
@@ -126,11 +126,10 @@ def search(self, q: Union[str, JQuery], k: int = 10, query_generator: JQueryGene
                 raise NotImplementedError('Cannot specify fields to search when using a Lucene query.')
             hits = self.object.search(q, k)
         else:
-            query_string = JString(q.encode('utf8'))
             if not fields:
-                hits = self.object.search(query_string, k)
-            else :
-                hits = self.object.searchFields(query_string, jfields, k)
+                hits = self.object.search(JString(q.encode('utf8')), k)
+            else:
+                hits = self.object.searchFields(JString(q.encode('utf8')), jfields, k)
 
         docids = set()
         filtered_hits = []