There has been exponential growth in the interest and availability within the last five years for the developments in predictive ADME modeling. The ambitious goal of those virtual assessments in target selection and maturation is to provide predictive tools to characterize and possibly drop compounds that are most likely to exhibit ADME or toxicity problems sooner. The impact of simulation and prediction in the success of drug discovery and drug development is here briefly presented. Starting from general consideration of what are a model and a prediction, arguments are presented that show that quality of models not only depends on development of computers and algorithms but also rely on the quality of the input data. In this sense, experimental and in silico aspects are very complementary. Evolution of the techniques, some benefits but also limits will be underlined. Among the topics that are covered, progresses in methods and technologies coupled with technical advances (e.g. power of computers) are summarized; evolution of predictive parameters used in pharmacophore elaboration and current limits of structure-based drug design or virtual screening approaches are presented; more recent challenging fields (e.g. polymorphism) are also presented.