Identification of prospective drugs via a computational approach represents a major challenge in drug discovery. More specifically, elimination of false positive produced in high throughput virtual screening campaigns are still one of the key problems in the early stages of drug design. In a hierarchical, funnel-shaped computational pipeline, hits from cost-effective docking or supervised machine learning approaches, before undergoing wet-lab assessment, are often purged of false positives using expensive molecular dynamics (MD) techniques with a full atomistic description of the system. Most of the current MD applications for computing the ligand-receptor binding affinities are based on the so-called alchemical approach using Free Energy Perturbation (FEP), whereby the build-up of ligand-environment interactions is assessed in the two legs of a thermodynamic cycle (the ligand in the bulk and in the bound state) by constructing a series of intermediate connecting alchemical states. The binding free energy between the physical end states in the two legs of the cycle is recovered as a sum of free energy difference between non physical alchemical states using free energy perturbation and the Bennett acceptance ratio or thermodynamic integration. The absolute binding free energy can hence essentially be computed as a difference between two solvation free energies, namely that of the ligand in bulk solvent and in the bound state. Alchemical techniques can be likewise adapted to evaluate the relative binding free energy between two congeneric compounds by gradually transmuting one compound into the other in a series of "chimeric" states in the two legs of the cycle. Despite progresses in the last decade of algorithms, force fields and methods, and despite the spectacular growth of computer power recently boosted by the advent of GPU-CPU heterogeneous high performing computing (HPC) architectures, alchemical techniques based on FEP still faces daunting computational challenges, mostly related to the difficulties in achieving a stationary (converged) sampling in conformationally complex drug receptor systems. In this respect, molecular recognition is widely thought to be driven by two main mechanisms, namely the inducedfit model or the conformational selection paradigm. According to the former, the protein adapts its conformation upon ligand binding, switching from an apo (unbound) to an holo (bound) form, while in the latter case, believed to be the cornerstone of the allosteric effect, binding is possible only when the protein is in one particular conformation among several metastable states in chemical equilibrium.