While a relative and approximate ranking of the stability of different complexes might be sufficient for an initial screening protocol, a finer and more accurate evaluation of the binding free energy may be necessary for a fine tuning in later stages of drug design. Free energy estimates require simulation of complex flexibility and desolvation upon binding of both partners in order to deduce the entropy term instead of a simple extrapolation of the enthalpy term with a very rough and partial prediction of the entropy part as in usual and quick affinity prediction methods. For the most accurate methods, very long simulations are required and limit their use. If accurate energies are needed, the methods of choice are sophisticated MD-based calculations, such as thermodynamic integration (TI) and free energy perturbation (FEP) . Since they are computationally very expensive, extremely time-consuming and exhaustive conformational and statistical sampling is needed to obtain converged results, they are not widely used in structure-based drug design. Among the approximate methods, there are the linear interaction energy (LIE) , the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and the related molecular mechanics generalized Born surface area (MM-GBSA) methods . LIE is a semi-empirical method, based on the assumption that the binding free energy between the ligand and the receptor can be modelled as a linear-response combining weighted electrostatic and Van der Waals interactions with coefficients varying for different systems . Unlike the LIE method, MM-PBSA and MM-GBSA do not employ empirical parameters within their calculations, which makes them promising methods for ranking very different compounds. They both use molecular mechanics force fields with continuum solvent models. The GB equation is simply an approximation of the PB equation , resulting in an increased calculation speed (about 5 times faster), but often goes along with an accuracy tradeoff . They are both frequently used in structure-based drug design due to their rather high accuracy and relative high computational efficiency. Another advantage is that they have no varying parameters for different protein-ligand systems while using sets of physically well-defined energy terms and they do not require training set calculations.