diff --git a/nlg/search.py b/nlg/search.py
index 6574b2d..716f6f1 100644
--- a/nlg/search.py
+++ b/nlg/search.py
@@ -46,9 +46,12 @@ def _sort_search_results(items, priorities=SEARCH_PRIORITIES):
         Prioritized search results - for each {token: search_matches} pair, sort
         search_matches such that a higher priority search result is enabled.
     """
-    match_ix = [[p.items() <= item.items() for p in priorities] for item in items]
-    min_match = [m.index(True) for m in match_ix]
-    items[min_match.index(min(min_match))]['enabled'] = True
+    if len(items) > 1:
+        match_ix = [[p.items() <= item.items() for p in priorities] for item in items]
+        min_match = [m.index(True) for m in match_ix]
+        items[min_match.index(min(min_match))]['enabled'] = True
+    else:
+        items[0]['enabled'] = True
     return items
 
 
@@ -230,6 +233,7 @@ def search(self, text, colname_fmt='df.columns[{}]',
             the source dataframe, and values are a list of locations in the df
             where they are found.
         """
+        self.search_nes(text)
         if len(text.text) <= _df_maxlen(self.df):
             for i in _text_search_array(text.text, self.df.columns):
                 self.results[text] = {'location': 'colname', 'tmpl': colname_fmt.format(i),
@@ -242,7 +246,6 @@ def search(self, text, colname_fmt='df.columns[{}]',
                     'type': 'doc'}
 
         else:
-            self.search_nes(text)
             for token, ix in self.search_columns(text, **kwargs).items():
                 ix = utils.sanitize_indices(self.df.shape, ix, 1)
                 self.results[token] = {'location': 'colname', 'tmpl': colname_fmt.format(ix),
diff --git a/nlg/tests/test_search.py b/nlg/tests/test_search.py
index 158a94c..3a5aaa7 100644
--- a/nlg/tests/test_search.py
+++ b/nlg/tests/test_search.py
@@ -107,6 +107,8 @@ class TestSearch(unittest.TestCase):
     def setUpClass(cls):
         fpath = op.join(op.dirname(__file__), "data", "actors.csv")
         cls.df = pd.read_csv(fpath, encoding='utf-8')
+        fpath = op.join(op.dirname(__file__), "data", "imdb_ratings.csv")
+        cls.imdb = pd.read_csv(fpath, encoding='utf-8')
 
     def test_dfsearches(self):
         x = search.DFSearchResults()
@@ -118,7 +120,6 @@ def test_dfsearches(self):
         x['hello'] = 'underworld'
         self.assertDictEqual(x, {'hello': ['world', 'underworld']})
 
-    # @unittest.skip("Temporary")
     def test_search_args(self):
         args = utils.sanitize_fh_args({"_sort": ["-votes"]}, self.df)
         doc = nlp("James Stewart is the top voted actor.")
@@ -145,9 +146,7 @@ def test_search_args_literal(self):
                                  "type": "token"}})
 
     def test_templatize(self):
-        fpath = op.join(op.dirname(__file__), "data", "actors.csv")
-        df = pd.read_csv(fpath, encoding='utf-8')
-        df.sort_values("votes", ascending=False, inplace=True)
+        df = self.df.sort_values("votes", ascending=False)
         df.reset_index(inplace=True, drop=True)
 
         doc = nlp("""
@@ -190,7 +189,6 @@ def test_templatize(self):
         )
 
     def test_search_sort(self):
-
         results = [
             {'tmpl': 'df.loc[0, "name"]', 'type': 'ne', 'location': 'cell'},
             {'tmpl': 'df.columns[0]', 'type': 'token', 'location': 'colname'},
@@ -236,17 +234,23 @@ def test_single_entity_search(self):
         self.assertEqual(variable.template, '{{ df["name"].iloc[0] }}')
 
     def test_literal_search(self):
-        df = pd.read_csv(
-            op.join(op.dirname(__file__), 'data', 'imdb_ratings.csv'), encoding='utf8')
         texts = ['How I Met Your Mother', 'Sherlock', 'Dexter', 'Breaking Bad']
         for t in texts:
             doc = nlp(t)
-            nugget = search.templatize(doc, {}, df)
+            nugget = search.templatize(doc, {}, self.imdb)
             self.assertEqual(len(nugget.tokenmap), 1)
             for token, variable in nugget.tokenmap.items():
                 self.assertEqual(token.text, t)
                 self.assertRegex(nugget.template, r'{{ df\["name"\].iloc\[-*\d+\] }}')
 
+    def test_search_short_strings(self):
+        # Check strings that are shorter than the max length of the df,
+        # but still not a literal match
+        nugget = search.templatize(nlp('Dexter is a good show'), {}, self.imdb)
+        self.assertEqual(len(nugget.tokenmap), 1)
+        token, variable = nugget.tokenmap.popitem()
+        self.assertRegex(variable.enabled_source['tmpl'], r'df\["name"\].iloc\[-*\d+\]')
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/nlg/utils.py b/nlg/utils.py
index 8b5780d..50cd0cc 100644
--- a/nlg/utils.py
+++ b/nlg/utils.py
@@ -8,7 +8,7 @@
 import re
 
 import pandas as pd
-from spacy.tokens import Token
+from spacy.tokens import Token, Doc, Span
 from tornado.template import Template
 
 from gramex.data import filter as gfilter  # NOQA: F401
@@ -111,12 +111,19 @@ def __call__(self, func):
 
 def is_overlap(x, y):
     """Whether the token x is contained within any span in the sequence y."""
+    if len(y) == 0:
+        return False
     if isinstance(x, Token):
         if x.pos_ == "NUM":
             return False
     elif 'NUM' in [c.pos_ for c in x]:
         return False
-    return any([x.text in yy.text for yy in y])
+    if len(y) > 1:
+        return any([x.text in yy.text for yy in y])
+    y = y.pop()
+    if isinstance(x, (Token, Span)) and isinstance(y, Doc):
+        return x.doc == y
+    return False
 
 
 def unoverlap(tokens):