Structure-based drug design aims to discover novel lead compounds and to predict the mode by which they bind to a receptor. Much of our earlier work focused on the elucidation of the different binding modes of a flexible ligand to a rigid receptor. Genetic algorithms were used to search the conformational space of the ligand within the receptor. Our results showed that genetic algorithms could be used with a molecular mechanics force field to successfully dock flexible ligands to rigid receptors. There are two obvious areas where the above system could be improved. First, the genetic algorithm scoring function needs to more adequately account for solvation effects. Furthermore, the efficiency of the system needs to be improved. The genetic algorithms converged too slowly and often to local minima in the scoring function. Our current work addresses each of the problems by introducing empirical scoring functions. These include van der Waals interactions, hydrogen bonding, and hydrophobic effects. Our results illustrate that these empirical fitness functions perform very well. Solvation effects are included explicitly, the energy evaluations are much more efficient, and the energy landscape is much smoother allowing the genetic algorithms to converge much more quickly. This work was greatly facilitated by the resources in the Computer Graphics laboratory. The MidasPlus and Chimera software enabled us to visualize the docking results and refine the empirical fitness functions. Additionally, we have had many helpful discussions about this work with members of the Computer Graphics Laboratory. This work was performed using the computer resources of the Computer Graphics Laboratory.