Inhomogeneous fluid solvation theory (IFST) is a statistical mechanical framework for calculating solvation free energies from molecular dynamics (MD) or Monte Carlo simulations. The solvation enthalpy is calculated from the potential energy function and the solvation entropy is calculated from the translational and orientational ordering of solvent molecules in the solute reference frame (solute-water correlation terms) and the translational and orientational ordering of solvent molecules relative to one another (water-water correlation terms). IFST has been applied to numerous systems and has proved particularly useful in understanding the determinants of binding affinity and designing new inhibitors in the lead optimization stages of drug development. One of the useful features of IFST is that free energy changes are calculated for small subvolumes surrounding the solute and this allows the contribution of different regions of space to be calculated and visualized. Work in this lab has focused on modeling water networks at protein surfaces and developing ISFT with quantitative accuracy. Here, we report hydration free energies calculated using IFST for a series of small molecules and compare the results with hydration free energies calculated using free energy perturbation (FEP). We incorporate a k-nearest neighbors (KNN) algorithm to compute the solvation entropy and demonstrate its superiority over a histogram method. Importantly, it is demonstrated that MD samples must be independent for effective use of the KNN algorithm. This finding impacts any application of the KNN algorithm to time series data. We also report from a lead-optimisation campaign using IFST to develop inhibitors for a protein-protein interaction and discuss the importance of identifying binding hotspots at the protein surface. Visualisation of relative free-energy density is an interesting and useful tool in understanding intermolecular interactions and visualisation of water networks is a key enabling technique in drug discovery.