This review is intended to describe some of the methods and procedures used for computer-aided drug design when the structure of the macromolecular target is unknown, as is the case for CNS active drugs. Strategies and methods used in computer-aided design of drugs in such instances must be "indirect," i.e., focusing on the characterization of the ligands themselves. This situation is different from one in which the three-dimensional structure of the macromolecular target for a drug is known, for example, for drugs that are enzyme inhibitors, allowing "direct" characterization of ligand-receptor interactions. Two qualitatively different "indirect" approaches are described here. One, called 2D-QSAR, is briefly reviewed. It is based on delineating regression relationships between a specified biological end point and properties of the compounds eliciting it. The other, based on pharmacophore development, constitutes the main part of this review. Several levels of pharmacophore development are described, which differ in the extent to which they encompass fundamental molecular properties that are determinants of receptor recognition and activation. The strengths and limitations of each procedure are discussed and illustrated by examples. Two methods for obtaining model receptor structures are then briefly described. Both rely on the prior success of the indirect methods in obtaining ligand properties that modulate receptor recognition and activation. These emerging capabilities have the potential to bridge the gap between indirect and direct methods of drug design, since, if successful, the design process can continue in a direct mode using explicit characterization of drug-receptor interactions. Strategies for hypothesis validation and use of hypothesis for drug design and discovery are also briefly reviewed. The final sections of this review describe specific computational tools such as molecular mechanics and quantum mechanical methods used to characterize and identify relevant molecular properties and indicate some areas for future development of computational chemistry methods that could increase its effectiveness in the design of novel drugs.