The advent of contextualised language models has brought gains in search effectiveness, not just when applied for re-ranking the output of classical weighting models such as BM25, but alsowhenused directly for passage indexing and retrieval, a technique which is called dense retrieval.In theexisting literature in neural ranking, two dense retrieval families have become apparent: singlerepresentation, where entire passages are represented by a single embedding (usually BERT’s [CLS] token, as exemplified by the recent ANCE approach) , or multiplerepresentations, where each token in a passage is represented by its own embedding (as exemplified by the recent ColBERT approach) .These two familieshave not been directly compared.However, because ofthe likely importance of dense retrieval moving forward, a clear understanding of their advantages and disadvantages isparamount. To this end, this paper contributes a direct study on their comparative effectiveness, noting situations where eachmethodunder/over performs w.r.t. each other, and w.r.t. a BM25 baseline.We observe that, while ANCE is more efficient than ColBERT in terms of response time and memory usage, multiple representations are statistically more effective than the single representations for MAP and MRR@10. We also show that multiple representations get better improvements than single representations for queries being the hardest for BM25, as well as for definitional queries, and those with complex information needs.