The majority of drug targets for small molecule therapeutics are proteins whose three-dimensional structure is not known to sufficient resolution to permit structure-based design. All three-dimensional QSAR approaches have a requirement for some hypothesis of ligand conformation and alignment, and predictions of molecular activity critically depend on this ligand-based binding site hypothesis. The molecular similarity function used in the Surflex docking system, coupled with quantitative pressure to minimize overall molecular volume, forms an effective objective function for generating hypotheses of bioactive conformations of sets of small molecules binding to their cognate proteins. Results are presented, assessing utility of the method for ligands of the serotonin, histamine, muscarinic, and GABA(A) receptors. The Surflex similarity module (Surflex-Sim) was able, in each case, to distinguish true ligands from random compounds using models constructed from just two or three known ligands. True positive rates of 60% were achieved with false positive rates of 0-3%; the theoretical enrichment rates were over 150-fold compared with random screening. The methods are practically applicable for rational design of ligands and for high-throughput virtual screening and offer competitive performance to many structure-based docking algorithms.