We have devised a chemocentric informatics methodology for drug discovery integrating independent approaches to mining biomolecular databases. As a proof of concept, we have searched for novel putative cognition enhancers. First, we generated Quantitative Structure-Activity Relationship (QSAR) models of compounds binding to 5-hydroxytryptamine-6 receptor (5-HT(6)R), a known target for cognition enhancers, and employed these models for virtual screening to identify putative 5-HT(6)R actives. Second, we queried chemogenomics data from the Connectivity Map ( http://www.broad.mit.edu/cmap/ ) with the gene expression profile signatures of Alzheimer's disease patients to identify compounds putatively linked to the disease. Thirteen common hits were tested in 5-HT(6)R radioligand binding assays and ten were confirmed as actives. Four of them were known selective estrogen receptor modulators that were never reported as 5-HT(6)R ligands. Furthermore, nine of the confirmed actives were reported elsewhere to have memory-enhancing effects. The approaches discussed herein can be used broadly to identify novel drug-target-disease associations.