A unique feature of the ensemble methodology is that it requires adapting the ensemble size to improve the precision of results, which in turn leads to better accuracy in several cases. To further clarify this point, we picked out the worst performing MCL1 system from our dataset. This protein system has something unique about it that makes it the worst performing not only with TIES but also with PMX while being second worst with FEP+. This behaviour is likely to be related to the flexible structure of a generic MCL1 ligand with its two ends (one hydrophobic, the other charged) connected with a 4-membered linker such that the hydrophobic end is buried deep into the lower pocket while the charged end interacts