The traditional drug design strategy centrally focuses on optimizing binding affinity with the receptor target and evaluates pharmacokinetic properties at a later stage which causes high rate of attrition in clin. trials.Alternatively, parallel screening allows evaluation of these properties and affinity simultaneously.In a case study to identify leads from natural compounds with exptl. HIV-1 reverse transcriptase (RT) inhibition, the authors integrated various computational approaches including Caco-2 cell permeability QSAR model with applicability domain (AD) to recognize drug-like natural compounds, mol. docking to study HIV-1 RT interactions and shape similarity anal. with known crystal inhibitors having characteristic butterfly-like model.Further, the lipophilic properties of the compounds refined from the process with best scores were examined using lipophilic ligand efficiency (LLE) index.Seven natural compound hits viz. baicalien, (+)-calanolide A, mniopetal F, fagaronine chloride, 3,5,8-trihydroxy-4-quinolone Me ether derivative, nitidine chloride and palmatine, were prioritized based on LLE score which demonstrated Caco-2 well absorption labeling, encompassment in AD structural coverage, better receptor affinity, shape adaptation and permissible AlogP value.The authors showed that this integrative approach is successful in lead exploration of natural compounds targeted against HIV-1 RT enzyme.