All the methods in this work use the same dual topology input systems. These systems model five proteins and 54 ligand transformations. The models are taken from the previous work of Bhati et al. and details of their preparation are provided in that paper. In the SI of this paper we provide all these parameterised systems and note here that the AMBER ff99SB-ILDN force field was used for protein parameters and the ligand parameters were produced using the general AMBER force field (GAFF) . Our alchemical protocol involves collecting sampling from 13 intermediate alchemical states. This entails running an energy minimization followed by 2 ns of equilibration. After running pre-production on each state, 4 ns of N P T production sampling is run. In each state an ensemble of five simulations is performed for each simulation leg to calculate one ∆G value. From the production sampling the potential and gradient, ∂u(λ,x) ∂λ , are calculated every 4 ps. For the FEP estimator based results presented in this work, each one of the replicas in the ensemble of five simulated allowed for the calculation of one ∆G by applying equation 3 to the potentials sampled from the simulation. The five resulting values of ∆G are then bootstrapped to calculate a mean and standard error of the mean (SEM). For the TI results we apply the TIES protocol as it has been used in previous work . The defining characteristic of TIES is the use of an ensemble of simulations in each alchemical state to control the aleatoric errors inherent to MD simulations. In every one of the total 13 alchemical states an ensemble of 5 simulation is performed, each of which yields a time series of ∂u(λ,x) ∂λ that can be averaged to give ∂u(λ,x) ∂λ λ