With the increasing power of supercomputers, the use of computational methods in the field of drug discovery has risen rapidly. In silico methods can support the identification of potential therapeutics by accelerating the process of screening vast real or virtual libraries of chemical compounds and/or de novo structures based on their binding strength to a given target protein. The binding affinity, also known as the binding free energy, is a quantitative measure of the strength of ligand-protein binding. Thus, computational methods for predicting binding free energies of ligand-protein complexes can play an important role in drug discovery. The average cost and time required to develop one drug stand at over $2 billion and 10 years respectively. Improving the reliability of such methods should substantially reduce the time and cost associated with bringing novel drugs to market. The urgent need to dramatically accelerate the process of drug discovery has been made manifest during the to the initial conditions given their chaotic nature. This is manifested in the fact that two independent MD trajectories diverge exponentially with time and explore very different microstates. It is this behaviour that confers the "mixing" ergodic property on MD simulation, a property stronger than ergodicity which is required to guarantee that the state of thermodynamic equilibrium may be reached. While one-off simulations, no matter how close their initial conditions in phase space, produce different results each time they are run, ensembles of such simulations produce a distribution of results whose properties (typically moments of the distribution including mean, variance and so on) are statistically robust.