Post-genomic era has led to the discovery of several new targets posing challenges for structure-based drug design efforts to identify lead compounds. Multiple computational methodologies exist to predict the high ranking hit/lead compounds. Among them, free energy methods provide the most accurate estimate of predicted binding affinity. Pathway-based Free Energy Perturbation (FEP), Thermodynamic Integration (TI) and Slow Growth (SG) as well as less rigorous end-point methods such as Linear interaction energy (LIE), Molecular Mechanics-Poisson Boltzmann./Generalized Born Surface Area (MM-PBSA/GBSA) and lambda-dynamics have been applied to a variety of biologically relevant problems. The recent advances in free energy methods and their applications including the prediction of protein-ligand binding affinity for some of the important drug targets have been elaborated. Results using a recently developed Quantum Mechanics (QM)/Molecular Mechanics (MM) based Free Energy Perturbation (FEP) method, which has the potential to provide a very accurate estimation of binding affinities to date has been discussed. A case study for the optimization of inhibitors for the fructose 1,6-bisphosphatase inhibitors has been described.