We sought to understand the reproducibility of the allocated sampling times between repeat runs of the entire protocol, and the effect of changing simulation parameters on the time allocated. Table shows the allocation of sampling time between protocols labelled according to their runtime constant (r, kcal 2 mol -2 ns -1 ), the thermodynamic speed used for window spacing (s, kcal mol -1 ), and the number of independently-equilibrated repeat runs per protocol (n). The overall free energies are generally not significantly different to the non-adaptive 30 ns result. There is reasonable consistency between the times allocated for repeats of the r0.005-s1-n5 protocol, showing that similar sampling issues and related uncertainties are detected between overall repeats. Sampling time is mostly concentrated around λ = 0.4 during the bound vanish stage, where water begins to enter the binding site. In the ideal case, where the uncertainty decreases proportionally to the square root of sampling time, all protocols with the same runtime constant should be allocated equal sampling times, regardless of the number of λ-windows or number of repeats. This appears to be the case for the free legs, where doubling the number of replicates, or halving or doubling the number of λ-windows does not appear to affect the allocated sampling time. In contrast, the bound vanish leg simulations are prone to becoming stuck in local minima and the uncertainties do not generally decrease as expected with additional sampling time (Section 4.1.1). Here, increasing the number of λ-windows (r0.0005-sOrig-n5, r0.0005-s0.5-n5) or the number of replicates (r0.0005-s1-n10) appears to decrease the total allocated sampling time, likely indicating that reduced uncertainty is achieved with equivalent sampling time when the number of independent simulations is greater, in agreement with Wan et al.. 127 However, this trend is not replicated for the bound discharge leg. Increasing the λ-spacing with speeds of 2 and 4 kcal mol -1 generally increased the total sampling time, but did not have a reliable effect on the sampling time for the bound stages, possibly indicating lower reliability of sampling issue detection with fewer independent simulations. Surprisingly, the r0.0005-s4-n5 result remained reasonable despite extremely poor overlap (for the bound vanish leg, the highest off-diagonal overlap was 0.02). When the runtime constant was reduced by a factor of 5, the time allocations for the free leg increased by a factor of approximately √ 5 as expected, although the proportional increase was greater for the bound stages. Overall, this indicates that the algorithm is robust and behaves as expected when uncertainties decrease in proportion to the square root of the sampling time, as assumed. However, when simulations become trapped in local minima, the overall sampling time allocation can be affected by parameters other than just the runtime constant. a ∆G o bind shown with with 95 % t-based confidence intervals). In the protocol names, r denotes the run time constant (kcal 2 mol -2 ns -1 ), s denotes the thermodynamic speed (kcal mol -1 ) used to determine the λ-window spacing, and n denotes the number of replicates in each ensemble run. sOrig denotes the use of the default λ-spacing. A detailed breakdown of the calculated free energies is given in Table .