Clearly, the quality of the force fields is one of the major factors in determining the outcomes in such campaigns. The calculated hydration free energy of a ligand is fully determined by two force fields: the water model and the small molecule force field, and their interplay. Over the past several decades, literally hundreds of water models have been proposed and developed; yet in biomolecular modeling, the rigid, fixed-charge, 3-point TIP3P model remains among the most widely used, if not the most widely used, despite its many known shortcomings. Similarly, the general Amber force field (GAFF ) and its subsequent refinements hold this position in simulating drug and drug-like molecules. More recently, a new 4-point rigid water model named OPC was developed by optimizing the point charge distribution in water that reproduces its bulk properties far more accurately than TIP3P; a 3-point version named OPC3 was developed as a compromise between speed and accuracy. It was hoped that an improvement in bulk property reproduction would also translate into an improvement in hydration free energy predictions and for small sets of organic molecules, the new water models did indeed outperform TIP3P . However, those studies use only a small number of ligands to carry out the validation and do not report how they select the molecules they use out of the 642 available for benchmarking from the FreeSolv data set -the largest and most comprehensive curated set of experimentally measured hydration free energies, mostly comprising small organic molecules. Here, we report a thorough analysis of the performance of hydration free energy calculations on the entire FreeSolv set for a combination of the GAFF2.11 force field with the TIP3P, OPC, and OPC3 water models, and compare and contrast all-atom molecular dynamics hydration free energy calculations to the more affordable two-dimensional quantitative structure -activity relationship (2D QSAR ) approach based on low-level molecular descriptors. We discuss their merits and shortcomings from the standpoint of computer-aided drug discovery and development. Moreover, we also compare the performance of GAFF2.11 with the previous version of GAFF -1.81 -demonstrating a robust means of validating edits to force fields that is far more relevant to drug discovery, design, and development than what physics focused research laboratories typically employ. We demonstrate that calculated HFEs become increasingly inaccurate with increases in ligand molecular weight and show that larger ligands require much longer simulations to converge -two findings with important implications for drug design and discovery.