Today, early characterization of drug properties by the Biopharmaceutics Classification System (BCS) has attracted significant attention in pharmaceutical discovery and development. In this direction, the present report provides a systematic study of the development of a BCS-based provisional classification (PBC) for a set of 322 oral drugs. This classification, based on the revised aqueous solubility and the apparent permeability across Caco-2 cell monolayers, displays a high correlation (overall 76%) with the provisional BCS classification published by World Health Organization (WHO). Current database contains 91 (28.3%) PBC class I drugs, 76 (23.6%) class II drugs, 97 (31.1%) class III drugs, and 58 (18.0%) class IV drugs. Other approaches for provisional classification of drugs have been surveyed. The use of a calculated polar surface area with a labetalol value as a high permeable cutoff limit and aqueous solubility higher than 0.1 mg/mL could be used as alternative criteria for provisionally classifying BCS permeability and solubility in early drug discovery. To develop QSPR models that allow screening PBC and BCS classes of new molecular entities (NMEs), 18 statistical linear and nonlinear models have been constructed based on 803 0-2D Dragon and 126 Volsurf+ molecular descriptors to classify the PBC solubility and permeability. The voting consensus model of solubility (VoteS) showed a high accuracy of 88.7% in training and 92.3% in the test set. Likewise, for the permeability model (VoteP), accuracy was 85.3% in training and 96.9% in the test set. A combination of VoteS and VoteP appropriately predicts the PBC class of drugs (overall 73% with class I precision of 77.2%). This consensus system predicts an external set of 57 WHO BCS classified drugs with 87.5% of accuracy. Interestingly, computational assignments of the PBC class reasonably correspond to the Biopharmaceutics Drug Disposition Classification System (BDDCS) allocations of drugs (accuracy of 63.3-69.8%). A screening assay has been simulated using a large data set of compounds in different drug development phases (1, 2, 3, and launched) and NMEs. Distributions of PBC forecasts illustrate the current status in drug discovery and development. It is anticipated that a combination of the QSPR approach and well-validated in vitro experimentations could offer the best estimation of BCS for NMEs in the early stages of drug discovery.