Acquired immunodeficiency syndrome (AIDS) is a life-threatening disease which is a collection of symptoms and infections caused by a retrovirus, human immunodeficiency virus (HIV). There is currently no curative treatment and therapy is reliant on the use of existing anti-retroviral drugs. Pharmacoinformatics approaches have already proven their pivotal role in the pharmaceutical industry for lead identification and optimization. In the current study, we analysed the binding preferences and inhibitory activity of HIV-integrase inhibitors using pharmacoinformatics. A set of 30 compounds were selected as the training set of a total 540 molecules for pharmacophore model generation. The final model was validated by statistical parameters and further used for virtual screening. The best mapped model (R = 0.940, RMSD = 2.847, Q(2) = 0.912, se = 0.498, Rpred(2) = 0.847 and rm(test)(2) = 0.636) explained that two hydrogen bond acceptor and one aromatic ring features were crucial for the inhibition of HIV-integrase. From virtual screening, initial hits were sorted using a number of parameters and finally two compounds were proposed as promising HIV-integrase inhibitors. Drug-likeness properties of the final screened compounds were compared to FDA approved HIV-integrase inhibitors. HIV-integrase structure in complex with the most active and final screened compounds were subjected to 50 ns molecular dynamics (MD) simulation studies to check comparative stability of the complexes. The study suggested that the screened compounds might be promising HIV-integrase inhibitors. The new chemical entities obtained from the NCI database will be subjected to experimental studies to confirm potential inhibition of HIV integrase.