We should emphasise here that the use of TIES does not restrict the validity of our findings. They are true for any RBFE method including, in particular, the so-called free energy perturbation (FEP) approach. Indeed, we have conducted a number of studies which confirm the general power of the approach and agreement with other methods within an ensemble based approach. Moreover, in a recent study we show that ensemble based FEP and TIES can be performed concurrently at little extra cost and their results will be statistically identical. The same general behaviour is also exhibited by non-equilibrium MD methods. Indeed, this was reported by Potterton et al. where it was found that an ensemble size of 10 was necessary to reliably predict relative residence times for ligands. This is equally applicable to other non-equilibrium methods including free energy methods such as those based on Jarzynski's inequality. Similarly, machine learning (ML) techniques are increasingly being employed in the field of free energy predictions. Of particular interest are studies combining ML and physicsbased methods to accelerate free energy predictions for the selection of potential therapeutics. Such approaches have considerable potential and may be applicable to binding free energy predictions too, but they have many of their own limitations that need to be overcome. One major limitation is that ML methods are heavily data dependent and hence, unless the data distributions of experimental data are understood and accounted for, such methods produce over confident predictions. These methods are beyond the scope of the present paper and will not be discussed further here.