The Quantitative Structure-Activity Relationship (QSAR) approach has been applied to model binding affinity and receptor subtype selectivity of human 5HT1E and 5HT1F receptor-ligands. The experimental data were obtained from the PDSP Ki Database. Several descriptor types and data-mining approaches have been used in the context of combinatorial QSAR modeling. Data mining approaches included k Nearest Neighbor, Automated Lazy Learning (ALL), and PLS; descriptor types included MolConnZ, MOE, DRAGON, Frequent Subgraphs (FSG), and Molecular Hologram Fingerprints (MHFs). Highly predictive QSAR models were generated for all three data sets (i.e., for ligands of both receptor subtypes and for subtype selectivity), and different individual techniques were proved best in each case. For real value activity data available for 5HT1E and 5HT1F ligand binding, models were characterized by leave-one-out cross-validated R(2) (q(2)) for the training sets and predictive R(2) values for the test sets. The best models for 5HT1E ligands were obtained with the kNN approach combined with MolConnZ descriptors (q(2)=0.69, R(2)=0.92); for 5HT1F ligands ALL QSAR method using MolConnZ descriptors gave the best results (R(2)=0.92). Rigorously validated classification models were also developed for the 5HT1E/5HT1F subtype selectivity data set with high correct classification accuracy for both training (CCRtrain=0.88) and test (CCRtest=1.00) sets using kNN with MolConnZ descriptors. The external predictive power of QSAR models was further validated by virtual screening of The Scripps Research Institute (TSRI) screening library to recover 5HT1E agonists and antagonists (not present in the original PDSP data set) with high enrichment factors. The successful development of externally predictive and interpretative QSAR models affords further design and discovery of novel subtype specific GPCR agents.