The high failure rate of drugs in the development phase requires a strategy to reduce risks by generating lead candidates from different chemical classes. We describe a new three-dimensional computational approach for lead evolution, based on multiple pharmacophore hypotheses. Using full conformational models for both active and inactive compounds, a large number of pharmacophore hypotheses are analyzed to select the set or "ensemble" of hypotheses that, when combined, is most able to discriminate between active and inactive molecules. The ensemble hypothesis is then used to search virtual chemical libraries to identify compounds for synthesis. This method is very rapid, allowing very large virtual libraries on the order of a million compounds to be filtered efficiently. In applying this method to alpha(1)-adrenergic receptor ligands, we have demonstrated lead evolution from heterocyclic alpha(1)-adrenergic receptor ligands to highly dissimilar active N-substituted glycine compounds. Our results also show that the active N-substituted glycines are part of our smaller filtered library and thus could have been identified by synthesizing only a portion of the N-substituted glycine library.