Searching online text collections can be both rewarding and frustrating. While valuable information can be found, typically many irrelevant documents are also retrieved, while many relevant ones are missed. Terminology mismatches between the user's query and document contents are a main cause of retrieval failures. Expanding a user's query with related words can improve search performances, but finding and using related words is an open problem. This research uses corpus analysis techniques to automatically discover similar words directly from the contents of the databases which are not tagged with part-of-speech labels. Using these similarities, user queries are automatically expanded, resulting in conceptual retrieval rather than requiring exact word matches between queries and documents. We are able to achieve a 7.6% improvement for TREC 5 queries and up to a 28.5% improvement on the narrow-domain Cystic Fibrosis collection. This work has been extended to multidatabase collections where each subdatabase has a collection-specific similarity matrix associated with it. If the best matrix is selected, substantial search improvements are possible. Various techniques to select the appropriate matrix for a particular query are analyzed, and a 4.8% improvement in the results is validated.