Quantum mechanical (QM) methods are becoming popular in computational drug design and development mainly because high accuracy is required to estimate (relative) binding affinities. For low- to medium-throughput in silico screening, (e.g., scoring and prioritizing a series of inhibitors sharing the same molecular scaffold) efficient approximations have been developed in the past decade, like linear scaling QM in which the computation time scales almost linearly with the number of basis functions. Furthermore, QM-based procedures have been used recently for determining protonation states of ionizable groups, evaluating energies, and optimizing molecular structures. For highthroughput in silico screening QM approaches have been employed to derive robust quantitative structure-activity relationship models. It is expected that the use of QM methods will keep growing in all phases of computer-aided drug design and development. However, extensive sampling of conformational space and treatment of solution of macromolecules are still limiting factors for the broad application of QM in drug design.