The optimization of lead compounds as viable drug candidates involves the optimization of their binding affinity towards the selected target. The binding affinity, K-a, is determined by the Gibbs energy of binding, DeltaG, which in turn is determined by the enthalpy, DeltaH, and entropy, DeltaS, changes (DeltaG = DeltaH - T DeltaS). In principle, many combinations of DeltaH and DeltaS values can give rise to the same DeltaG value and, therefore, elicit the same binding affinity. However, enthalpically dominated ligands do not behave the same as entropically dominated ligands. Current paradigms in drug design usually generate highly hydrophobic and conformationally constrained ligands. The thermodynamic signature of these ligands is an entropically dominated binding affinity often accompanied by an unfavorable binding enthalpy. Conformationally constrained ligands cannot easily adapt to changes in the geometry of the binding site, being therefore highly susceptible to drug resistance mutations or naturally occurring genetic polymorphisms. The design of ligands with the capability to adapt to a changing target requires the introduction of certain elements of flexibility or the relaxation of some conformational constraints. Since these compounds pay a larger conformational entropy penalty upon binding, the optimization of their binding affinity requires the presence of a favorable binding enthalpy. In this paper, experimental and computational strategies aimed at identifying and optimizing enthalpic ligands will be discussed and applied to the case of HIV-1 protease inhibitors. It is shown that a thermodynamic guide to drug design permits the identification of drug candidates with a lower susceptibility to target mutations causing drug resistance.