diff --git a/jax/_src/numpy/lax_numpy.py b/jax/_src/numpy/lax_numpy.py
index 029f4f1d9942..3a937fa72efc 100644
--- a/jax/_src/numpy/lax_numpy.py
+++ b/jax/_src/numpy/lax_numpy.py
@@ -7037,10 +7037,13 @@ def _searchsorted_via_compare_all(sorted_arr: Array, query: Array, side: str, dt
         'sort' is often more performant on accelerator backends like GPU and TPU
         (particularly when ``v`` is very large), and 'compare_all' can be most performant
         when ``a`` is very small."""))
-@partial(jit, static_argnames=('side', 'sorter', 'method'))
+@partial(jit, static_argnames=('side', 'method'))
 def searchsorted(a: ArrayLike, v: ArrayLike, side: str = 'left',
                  sorter: None = None, *, method: str = 'scan') -> Array:
-  util.check_arraylike("searchsorted", a, v)
+  if sorter is None:
+    util.check_arraylike("searchsorted", a, v)
+  else:
+    util.check_arraylike("searchsorted", a, v, sorter)
   if side not in ['left', 'right']:
     raise ValueError(f"{side!r} is an invalid value for keyword 'side'. "
                      "Expected one of ['left', 'right'].")
@@ -7048,11 +7051,11 @@ def searchsorted(a: ArrayLike, v: ArrayLike, side: str = 'left',
     raise ValueError(
         f"{method!r} is an invalid value for keyword 'method'. "
         "Expected one of ['sort', 'scan', 'scan_unrolled', 'compare_all'].")
-  if sorter is not None:
-    raise NotImplementedError("sorter is not implemented")
   if ndim(a) != 1:
     raise ValueError("a should be 1-dimensional")
   a, v = util.promote_dtypes(a, v)
+  if sorter is not None:
+    a = a[sorter]
   dtype = int32 if len(a) <= np.iinfo(np.int32).max else int64
   if len(a) == 0:
     return zeros_like(v, dtype=dtype)
diff --git a/tests/lax_numpy_test.py b/tests/lax_numpy_test.py
index 050f39cedd15..18dec99a7fc9 100644
--- a/tests/lax_numpy_test.py
+++ b/tests/lax_numpy_test.py
@@ -2660,13 +2660,23 @@ def np_fun(arg):
     side=['left', 'right'],
     dtype=number_dtypes,
     method=['sort', 'scan', 'scan_unrolled', 'compare_all'],
+    use_sorter=[True, False],
   )
-  def testSearchsorted(self, ashape, vshape, side, dtype, method):
+  def testSearchsorted(self, ashape, vshape, side, dtype, method, use_sorter):
     rng = jtu.rand_default(self.rng())
-    args_maker = lambda: [np.sort(rng(ashape, dtype)), rng(vshape, dtype)]
-    def np_fun(a, v):
-      return np.searchsorted(a, v, side=side).astype('int32')
-    jnp_fun = lambda a, v: jnp.searchsorted(a, v, side=side, method=method)
+    def args_maker():
+      a = rng(ashape, dtype)
+      v = rng(vshape, dtype)
+      if use_sorter:
+        i = np.argsort(a)
+        return a, v, i
+      else:
+        a.sort()
+        return a, v
+    def np_fun(a, v, sorter=None):
+      return np.searchsorted(a, v, side=side, sorter=sorter).astype('int32')
+    def jnp_fun(a, v, sorter=None):
+      return jnp.searchsorted(a, v, side=side, method=method, sorter=sorter)
     self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker)
     self._CompileAndCheck(jnp_fun, args_maker)