Another source of error in free energy estimates comes from finite sampling. Current computational power allows reaching microsecond simulation timescales, yet in large scale free energy scans shorter sampling (up to tens of nanoseconds) is often employed. Depending on the system, such short sampling times may not be sufficient to converge the populations along the relevant degrees of freedom, e.g. ligand pose changes, amino acid rotamer motions, water positions in the binding site. Therefore, the limited sampling does not always ensure proper representation of the thermodynamic ensemble underlying the modelled system. This issue may be minimized by employing enhanced sampling protocols 11 such as replica exchange, metadynamics/local elevation, umbrella sampling or well designed sampling (MC) moves. Finally, uncertainty in the experimental measurements for the reference data limits the achievable prediction accuracy. Typically, one compares the result of calculations to the experimentally measured bioactivity data, which itself has error and is only an approximation or model to the ideal or true affinity. Additionally, the experimental data might be unsuitable for comparison, because the experimental conditions differ from the simulation conditions (e.g. the temperature), or the experiment did not measure the same observable (e.g phenotypic vs functional assays). To keep this error low, one should use high-quality and well curated data for the comparison and above all appreciate the maximum expected performance given the underlying experimental error. While some analyses suggest that the sampling, force field and experimental errors might contribute in a quantitatively similar manner, generally, the magnitude of each source of error is unknown and will likely be case dependent. In the current work we concentrate on quantifying force field related errors by comparing six small molecule molecular mechanics force fields in a benchmark of relative protein-ligand binding free energy calculations. For each force field we obtained to 1116 ∆∆G estimates across 22 protein targets. The large and diverse set of systems allows comparing not only distinct force field families -GAFF, CGenFF, OPLS, OpenFF -but also different versions of OpenFF: v1.0, v1.2, v2.0. With OpenFF presenting a novel direction in force field development, here, we demonstrate the ability of this force field to deliver high accuracy binding free energy predictions.