High concentrations of human non pancreatic secretory phospholipase A2 (hnps-PLA2) have been reported as inducing factors in different inflammatory diseases. Thus, hnps-PLA2 inhibitors would be potential drugs against disorders generated by high levels of this enzyme. The latter has been crystallized with different ligands and several classes of inhibitors are known, but the optimization of their therapeutic properties requires: (i) a better understanding of the inhibitorprotein interaction mechanism, and (ii) finding a strategy to predict the activity of new molecules. Approaches related to computational chemistry may help to resolve these two problems. An automated docking study was performed on a series of 188 competitive hnps-PLA2 inhibitors. The docking data were then used to establish 3D QSAR models by combining Comparative Molecular Field Analysis (CoMFA) and PLS modeling. The robustness and prediction power of the best model were assessed with help of cross-validation and test set procedures that delivered excellent scores. The search for the best inhibitors against hnps-PLA2 has to be associated with a high specificity of the molecules selected, minimizing possible human side effects. This requires keeping at an extremely low level the inhibitors activity against human pancreatic phospholipase A2 (hp-PLA2) which is in negligible concentration in all tissues except in pancreatic ones. Then, the above mentioned modeling procedure was applied also on a series of hp-PLA2 inhibitors and, once more, the 3D QSAR model thus generated showed an excellent robustness and prediction power. Finally, the combination of the two models generated on hnps-PLA2 and hp-PLA2 offered a global predictive tool able to select new strong anti-inflammatory drugs with negligible side effects, at least at pancreatic level.