Clearly, ABFE calculations must become more efficient to realise their potential. A promising class of methods yield relative binding free energies between structurally dissimilar ligands while aiming to avoid the sampling issues associated with "emptying"' the binding site. Approaches combining active learning of affinity predictions with free energy calculations have demonstrated substantial efficiency improvements by reducing the number of simulations required to identify potent binders. Non-equilibrium calculations involving "swarms" of short switching trajectories may decrease wall-clock time in some cases. A plethora of enhanced sampling techniques have been applied to alchemical free energy calculations, in some cases producing dramatic improvements in accuracy and efficiency. However, many of these techniques require system-specific tuning, yield highly system-specific benefits, may require frequent communication between otherwise independent replicas, and may even degrade performance in some cases. There are also areas in the standard ABFE cycle where substantial human and computer time is often wasted through manual set-up or poor defaults, and where automation and adaptive algorithms may offer substantial efficiency improvements. This avenue for efficiency improvement is much less well-explored than the approaches mentioned above and may be particularly beneficial for ABFE calculations, which vary widely in terms of sampling requirements and convergence behaviour. Here, we investigate three of these areas.