Virtual fragment screening (VFS) is a promising new method that uses computer models to identify small, fragment-like biologically active molecules as useful starting points for fragment-based drug discovery (FBDD). Training sets of true active and inactive fragment-like molecules to construct and validate target customized VFS methods are however lacking. We have for the first time explored the possibilities and challenges of VFS using molecular fingerprints derived from a unique set of fragment affinity data for the histamine H₃ receptor (H₃R), a pharmaceutically relevant G protein-coupled receptor (GPCR). Optimized FLAP (Fingerprints of Ligands and Proteins) models containing essential molecular interaction fields that discriminate known H₃R binders from inactive molecules were successfully used for the identification of new H₃R ligands. Prospective virtual screening of 156 090 molecules yielded a high hit rate of 62% (18 of the 29 tested) experimentally confirmed novel fragment-like H ₃R ligands that offer new potential starting points for the design of H₃R targeting drugs. The first construction and application of customized FLAP models for the discovery of fragment-like biologically active molecules demonstrates that VFS is an efficient way to explore protein-fragment interaction space in silico.