In this paper we discuss a hybrid approach combining Case-Based Reasoning (CBR) and Information Retrieval (IR) for the retrieval of full-text documents. Our hybrid CBR-IR approach takes as input a standard symbolic representation of a problem case and retrieves texts of relevant cases from a document collection dramatically larger than the case base available to the CBR system. Our system works by first performing a standard HYPO-style CBR analysis and then using the texts associated with certain important classes of cases found in this analysis to “seed” a modified version of INQUERY's relevance feedback mechanism in order to generate a query composed of individual terms or pairs of terms. Our approach provides two benefits: it extends the reach of CBR (for retrieval purposes) to much larger corpora, and it enables the injection of knowledge-based techniques into traditional IR. We describe our CBR-IR approach and report on on-going experiments.