The λ schedule from Clark et al., which was manually optimised for the MIF/MIF180 system, was used as a default. This used 8 windows for the free discharge stage, 18 for the free vanish stage, 6 for the bound restrain stage, 8 for the bound discharge stage, and 36 for the bound vanish stage. This schedule was used to run all non-adaptive simulations for the initial test systems. For adaptive simulations, the λ schedule at each stage was generated by running very short simulations (0.1 ns, no replicates) at all default λ values. These were used to estimate σ ∂H ∂λ as a function of λ, and the new λ schedule was generated so that the area under the σ ∂H ∂λ vs λ curve was equal between adjacent λ values. The size of these areas, and hence the spacing of the windows, were determined by the user-specified "thermodynamic speed" parameter. The adaptive runs began with 0.2 ns simulations for all replicates. Per-window free energy changes were calculated from the product of ∂H ∂λ λ and weights from the trapezoidal rule. From the inter-run deviations, the standard errors of the mean free energy changes were calculated and used to predict the optimal run times according to Equation . Additional simulation time was then allocated accordingly, and iterations of sampling time allocation and analysis were performed until predicted optimum sampling times agreed with the allocated sampling times. Equal sampling times were allocated to all replicate windows at the same value of λ. To promote smooth convergence to the "optimal" time, the total sampling time for each window was no more than doubled during each cycle. To smooth the allocation of sampling time between windows, it was enforced that the total sampling time for a given window could be no less than half that of the adjacent windows. Total sampling time was controlled with the user-input runtime constant (C in Equation ). To obtain true optimum efficiency, times and statistical inefficiencies should be measured in terms of computing time (or power consumption/ financial cost), rather than in nanoseconds of simulation time. This accounts for the differences in computational costs of different systems. To account for the different computational costs of different legs for different systems, t Current,k and t Optimal,k were multiplied by the relative computational cost of the current calculation leg, resulting in an overall factor of 1 √ Rel.Cost. on the right hand side of Equation . Costs were calculated for all systems from the λ selection simulations, which were all run on the same machine with four GeForce RTX 2080 SUPER GPUs. These were expressed relative to the MIF180 bound leg, which had an absolute computational cost of 0.21 hours ns -1 . Hence, Equation was used as shown with simulation times for the MIF180 bound legs, but where the com-putational cost was greater, the predicted optimal time was reduced. The approach of using relative costs was intended to improve the repeatability of the workflow -once users have calculated the absolute cost of the MIF180 bound leg, for which input files are supplied, this can be supplied to A3FE and the resultant simulation times (in ns) should be comparable to those shown here. The effect of varying hardware was removed by expressing computational time in GPU hours on GeForce RTX 2080 SUPER GPUs, obtained by multiplying simulation time in ns by the absolute cost calculated during the λ selection simulations.