De-novo drug design (DND) is a complex procedure, requiring the satisfaction of many pharmaceutically important objectives. Several computational methodologies employing various optimization approaches have been developed to search for satisfactory solutions to this multi-objective problem varying from composite methods, which transform the problem to a single objective one to Pareto methods searching for numerous solutions compromising the objectives. In this review we initially focus on the DND problem and the challenges it poses to computational methods, followed by an examination of the reported methodologies and specific applications. Emphasis is placed on the multiobjective nature of the problem, related considerations and the solutions proposed by the drug discovery community.