An unexplained overestimation is observed in the TIES study for one subset of compound pairs which share a common difference between the pairs. For the transformations to which cycle closure methods can be applied for the error estimation, a cancellation has been observed as the errors are similar for specific legs within the cycle. This results in an overall hysteresis near 0 for a closed cycle, but significant errors remain for individual legs. While continuing improvements in the force fields and the applications of ensemble methods are essential to generate increased accuracy for the physicsdriven alchemical approaches, machine learning methods can help to identify the discrepancies between the predictions and experimental data, and subsequently correct the differences. Despite the existence of this discrepancy, an excellent correlation is obtained in the free energy differences between the calculations and the experiments, indicating that the TIES approach can be used in prospective drug discovery projects to predict the changes of binding free energies in the lead optimisation process.