diff --git a/asv_bench/benchmarks/groupby.py b/asv_bench/benchmarks/groupby.py
index cf6f3f92068e8..1152c2253c42d 100644
--- a/asv_bench/benchmarks/groupby.py
+++ b/asv_bench/benchmarks/groupby.py
@@ -677,6 +677,15 @@ def time_groupby_extra_cat_sort(self):
     def time_groupby_extra_cat_nosort(self):
         self.df_extra_cat.groupby("a", sort=False)["b"].count()
 
+    def time_series_groupby_value_counts_many_categories(self):
+        df = self.df_extra_cat[0:10**4]
+        df.groupby("b")["a"].value_counts()
+
+    def time_series_groupby_value_counts_few_categories(self):
+        df = self.df_extra_cat[0:10**4].copy()
+        df["a"] = df["a"].cat.remove_unused_categories()
+        df.groupby("b")["a"].value_counts()
+
 
 class Datelike:
     # GH 14338
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 931d18dc349f3..cd2126826866f 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -468,6 +468,7 @@ Performance improvements
 - Performance improvement when setting values in a pyarrow backed string array (:issue:`46400`)
 - Performance improvement in :func:`factorize` (:issue:`46109`)
 - Performance improvement in :class:`DataFrame` and :class:`Series` constructors for extension dtype scalars (:issue:`45854`)
+- Performance improvement in :meth:`SeriesGroupBy.value_counts` with categorical values. (:issue:`46202`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_150.bug_fixes:
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 245e33fb1a23b..5b79e37586b42 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -609,12 +609,35 @@ def value_counts(
 
         names = self.grouper.names + [self.obj.name]
 
-        if is_categorical_dtype(val.dtype) or (
-            bins is not None and not np.iterable(bins)
-        ):
+        if is_categorical_dtype(val.dtype):
+            df = self.obj.to_frame()
+            df.columns = Index([self.obj.name])
+            # GH38672 relates to categorical dtype
+            groupby = DataFrameGroupBy(
+                df,
+                self.grouper,
+                axis=self.axis,
+                level=self.level,
+                grouper=self.grouper,
+                exclusions=self.exclusions,
+                as_index=self.as_index,
+                sort=self.sort,
+                group_keys=self.group_keys,
+                squeeze=self.squeeze,
+                observed=self.observed,
+                mutated=self.mutated,
+                dropna=self.dropna,
+            )
+            ser = groupby.value_counts(
+                normalize=normalize, sort=sort, ascending=ascending
+            )
+            ser.name = self.obj.name
+            return ser
+
+        if bins is not None and not np.iterable(bins):
             # scalar bins cannot be done at top level
             # in a backward compatible way
-            # GH38672 relates to categorical dtype
+
             ser = self.apply(
                 Series.value_counts,
                 normalize=normalize,