diff --git a/pyterrier_dr/__init__.py b/pyterrier_dr/__init__.py
index d860373..90bede9 100644
--- a/pyterrier_dr/__init__.py
+++ b/pyterrier_dr/__init__.py
@@ -5,7 +5,7 @@
 from pyterrier_dr.flex import FlexIndex
 from pyterrier_dr.biencoder import BiEncoder, BiQueryEncoder, BiDocEncoder, BiScorer
 from pyterrier_dr.hgf_models import HgfBiEncoder, TasB, RetroMAE
-from pyterrier_dr.sbert_models import SBertBiEncoder, Ance, Query2Query, GTR
+from pyterrier_dr.sbert_models import SBertBiEncoder, Ance, Query2Query, GTR, E5
 from pyterrier_dr.tctcolbert_model import TctColBert
 from pyterrier_dr.electra import ElectraScorer
 from pyterrier_dr.bge_m3 import BGEM3, BGEM3QueryEncoder, BGEM3DocEncoder
@@ -14,5 +14,5 @@
 
 __all__ = ["FlexIndex", "DocnoFile", "NilIndex", "NumpyIndex", "RankedLists", "FaissFlat", "FaissHnsw", "MemIndex", "TorchIndex",
            "BiEncoder", "BiQueryEncoder", "BiDocEncoder", "BiScorer", "HgfBiEncoder", "TasB", "RetroMAE", "SBertBiEncoder", "Ance",
-           "Query2Query", "GTR", "TctColBert", "ElectraScorer", "BGEM3", "BGEM3QueryEncoder", "BGEM3DocEncoder", "CDE", "CDECache",
+           "Query2Query", "GTR", "E5", "TctColBert", "ElectraScorer", "BGEM3", "BGEM3QueryEncoder", "BGEM3DocEncoder", "CDE", "CDECache",
            "SimFn", "infer_device", "AveragePrf", "VectorPrf"]
diff --git a/pyterrier_dr/pt_docs/index.rst b/pyterrier_dr/pt_docs/index.rst
index e6dfc55..92328c2 100644
--- a/pyterrier_dr/pt_docs/index.rst
+++ b/pyterrier_dr/pt_docs/index.rst
@@ -42,3 +42,4 @@ Import ``pyterrier_dr``, load a pre-built index and model, and retrieve:
     :maxdepth: 1
 
     prf
+    sbert
diff --git a/pyterrier_dr/pt_docs/sbert.rst b/pyterrier_dr/pt_docs/sbert.rst
new file mode 100644
index 0000000..fb32ea7
--- /dev/null
+++ b/pyterrier_dr/pt_docs/sbert.rst
@@ -0,0 +1,24 @@
+Using Sentence Transformer models for Dense retrieval in PyTerrier
+==================================================================
+
+With PyTerrier_DR, its easy to support Sentence Transformer (formerly called SentenceBERT)
+models, e.g. from HuggingFace, for dense retrieval.
+
+The base class is ``SBertBiEncoder('huggingface/path')``;
+
+There are easy to remember classes for a number of standard models:
+ - ANCE - an early single-representation dense retrieval model: ``Ance.firstp()``
+ - GTR - a dense retrieval model based on the T5 pre-trained encoder: ``GTR.base()``
+ - `E5 <https://huggingface.co/intfloat/e5-base-v2>`: ``E5.base()``
+ - Query2Query - a query similarity model: Query2Query()
+
+The standard pyterrier_dr pipelines can be used:
+
+Indexing::
+    model = pyterrier_dr.GTR.base()
+    index = pyterrier_dr.FlexIndex('gtr.flex')
+    pipe = (model >> index)
+    pipe.index(pt.get_dataset('irds:msmarco-passage').get_corpus_iter())
+
+Retrieval::
+    pipe.search("chemical reactions")
diff --git a/pyterrier_dr/sbert_models.py b/pyterrier_dr/sbert_models.py
index 24166cd..00ad8be 100644
--- a/pyterrier_dr/sbert_models.py
+++ b/pyterrier_dr/sbert_models.py
@@ -2,19 +2,26 @@
 import torch
 import pyterrier as pt
 from transformers import AutoConfig
+from functools import partialmethod
 from .biencoder import BiEncoder, BiQueryEncoder
 from .util import Variants
 from tqdm import tqdm
 
-def _sbert_encode(self, texts, batch_size=None):
+def _sbert_encode(self, texts, batch_size=None, prompt=None, normalize_embeddings=False):
     show_progress = False
     if isinstance(texts, tqdm):
         texts.disable = True
         show_progress = True
     texts = list(texts)
+    if prompt is not None:
+        texts = [prompt + t for t in texts]
     if len(texts) == 0:
         return np.empty(shape=(0, 0))
-    return self.model.encode(texts, batch_size=batch_size or self.batch_size, show_progress_bar=show_progress)
+    return self.model.encode(texts, 
+                             batch_size=batch_size or self.batch_size, 
+                             show_progress_bar=show_progress,
+                             normalize_embeddings=normalize_embeddings
+                             )
 
 
 class SBertBiEncoder(BiEncoder):
@@ -52,6 +59,16 @@ class Ance(_SBertBiEncoder):
         'firstp': 'sentence-transformers/msmarco-roberta-base-ance-firstp',
     }
 
+class E5(_SBertBiEncoder):
+
+    encode_queries = partialmethod(_sbert_encode, prompt='query: ', normalize_embeddings=True)
+    encode_docs = partialmethod(_sbert_encode, prompt='passage: ', normalize_embeddings=True)
+
+    VARIANTS = {
+        'base' : 'intfloat/e5-base-v2',
+        'small': 'intfloat/e5-small-v2', 
+        'large': 'intfloat/e5-large-v2',
+    }
 
 class GTR(_SBertBiEncoder):
     VARIANTS = {
diff --git a/tests/test_models.py b/tests/test_models.py
index 29b9c27..ff88b94 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -152,6 +152,13 @@ def test_ance(self):
         from pyterrier_dr import Ance
         self._base_test(Ance.firstp())
 
+    def test_e5(self):
+        from pyterrier_dr import E5
+        testmodel = E5.base()
+        self._base_test(testmodel)
+        inp = pd.DataFrame([{'qid': 'q1', 'query' : 'chemical reactions', 'docno' : 'd2', 'text' : 'professor proton mixed the chemical'}])
+        self.assertTrue(testmodel(inp).iloc[0]['score'] > 0)
+
     def test_tasb(self):
         from pyterrier_dr import TasB
         self._base_test(TasB.dot())