Textual entailment has recently been proposed as an application independent task of recognising whether the meaning of one text may be inferred from another. This is potentially a key task in many NLP applications. In this contribution, we investigate the use of various lexical entailment models in Information Retrieval, using the language modelling framework. We show that lexical entailment potentially provides a significant boost in performance, similar to pseudo-relevance feedback, but at a lower computational cost. In addition, we show that the performance is relatively stable with respect to the corpus the lexical entailment measure is estimated on.