The EVA molecular descriptor derived from calculated molecular vibrational frequencies is validated for use in QSAR studies. EVA provides a conformationally sensitive but, unlike 3D-QSAR methods such as CoMFA, superposition-free descriptor that has been shown to perform well with a wide range of datasets and biological endpoints. A detailed study is made using a benchmark steroid dataset with a training/test set division of structures. Intensive statistical validation tests are undertaken including various forms of crossvalidation and repeated random permutation testing. Latent variable score plots show that the distribution of structures in reduced dimensional space can be rationalized in terms of activity classes and that EVA is sensitive to structural inconsistencies. Together, the findings indicate that EVA is a statistically robust means of detecting structure-activity correlations with performance entirely comparable to that of analogous CoMFAs. The EVA descriptor is shown to be conformationally sensitive and as such can be considered to be a 3D descriptor but with the advantage over CoMFA that structural superposition is not required. EVA has the property that in certain situations the conformational sensitivity can be altered through the appropriate choice of the EVA sigma parameter.