We have ranked 13 different TIBO derivatives with respect to their relative free energies of binding using two approximate computational methods: adaptive chemical Monte Carlo/molecular dynamics (CMC/MD) and Poisson-Boltzmann/solvent accessibility (PB/SA) calculations. Eight of these derivatives have experimentally determined binding affinities. The remaining new derivatives were constructed based on contour maps around R86183 (8Cl-TIBO), generated with the program PROFEC (pictorial representation of free energy changes). The rank order among the derivatives with known binding affinity was in good agreement with experimental results for both methods, with average errors in the binding free energies of 1. 0 kcal/mol for CMC/MD and 1.3 kcal/mol for the PB/SA method. With both methods, we found that one of the new derivatives was predicted to bind 1-2 kcal/mol better than R86183, which is the hitherto most tightly binding derivative. This result was subsequently supported by the most rigorous free energy computational methods: free energy perturbation (FEP) and thermodynamic integration (TI). The strategy we have used here should be generally useful in structure-based drug optimization. An initial ligand is derivatized based on PROFEC suggestions, and the derivatives are ranked with CMC/MD and PB/SA to identify promising compounds. Since these two methods rely on different sets of approximations, they serve as a good complement to each other. Predictions of the improved affinity can be reinforced with FEP or TI and the best compounds synthesized and tested. Such a computational strategy would allow many different derivatives to be tested in a reasonable time, focusing synthetic efforts on the most promising modifications.