Human Acyl-CoA cholesterol acyltransferase (ACAT) plays an important role in catalysis of reaction which converts cholesterol into cholesteryl esters and long-chain fatty acyl coenzyme A. The inhibition of ACAT has therapeutically potential roles in hypercholestrolemia, atherosclerosis and coronary heart disease. For better understanding of essential chemical features for ACAT inhibition and identifying novel inhibitors, a three-dimensional (3D) chemical-feature-based quantitative QSAR pharmacophore model for available ACAT inhibitors have been developed for first time using Discovery Studio 2.5. The best model (Hypo1) having lowest total cost (84.14), highest cost difference (69.67), highest correlation coefficient (0.94), and lowest RMS (1.15Å), constitutes of one hydrogen bond acceptor, one hydrogen bond donor, two hydrophobic aromatic and one hydrophobic aliphatic feature. Validation of Hypo1 was further done using test set activity prediction, Fischer’s randomization method and decoy data set to check the reliability of the model. The validated Hypo1 was then used as a 3D search query for virtual screening to retrieve potential inhibitors from National cancer institute (NCI), ChemDiv and Specs databases. Finally, ADMET properties of selected compounds were calculated. The result shows the good potential of the newly found ACAT inhibitors. Finally, the two compounds have been obtained as novel hits to design the Novel ACAT inhibitors.