We found that the quantitative degree of chirality of substrates correlates with their efficiency of reaction with active sites. The degree of chirality, a global share descriptor, was determined by the use of the Continuous Chirality Measure (CCM) methodology developed previously (Zabrodsky et aI, J. Am. Chem. Sec. 1995, 117, 462), which treats chirality as a continuous structural properly and not as a binary quality (chiral/ not chiral). The generality of this new type of shape-activity correlation is demonstrated for five receptor/ substrate systems: trypsin/arylammonium inhibitors; the D-2-dopamine receptor/dopamine derivative agonists; trypsin/organophosphate inhibitors; acetylcholinesterase/organophosphates; and butyrylcholinesterase/organophosphates. The correlations were obtained both fur active-site induced chiral conformers and for inherently chiral inhibitors. interestingly, for some of these cases the correlation of activity with structure is hidden when classical parameters, such as chain length, are taken, but is revealed with this shape descriptor. For two cases we show that the CCM approach is capable of corroborating the assignment of the pharmacophore moiety. We define and make a distinction between the quantitative enantioselectivity ratio, which is the ratio of the slopes of the correlation lines for two enantiomeric series and which serves as a measure of enantioselectivity, and the quantitative chirality-sensitivity ratio, which compares the sensitivity to chirality changes of different enzymes toward the same set of inhibitors. The findings of this study are quite nontrivial because symmetry and chirality are global shape parameters and not specific descriptors of the intricate geometry of the drug or of the active site. We propose tentatively that these results may indicate two different recognition mechanisms: shape recognition and chemical recognition. The first is a low-resolution determination of the overall shape of the substrate and the second is the classical exact key-locking. We discuss possible implications of these results for predicting optimal shape from data of large libraries.