The objective here is to present the results of our study which show that rough approximations contribute to the improvement of recall in information retrieval (IR). The information retrieval literature provides ample evidence of situations in which less than 40% of the relevant documents are retrieved. A major reason for this is the problem of search vocabulary and the burden it imposes on the user who is expected to specify all possible terms that refer to the subject of interest. The theory of rough sets provides a framework for organizing the vocabulary in such a way that this constraint is reduced. The model also provides a set of search strategies that are flexible and user oriented. These strategies are based on approximate descriptions of objects such as queries and documents. This study concludes that retrieval strategies within the rough set model perform significantly better than retrieval within the vector model using the cosine formula, in document ranking as well as in recall. The paper concludes by demonstrating a methodology for document clustering using rough sets. This work is a continuation of earlier work by the author on the application of rough sets to information retrieval.