Despite these benefits, it is still challenging to run FE campaigns due to a lack of easily accessible automatized setup tools. The lack of such tools makes executing campaigns challenging for non-experts, and also inhibits development of larger-scale approaches to FE calculations. Additionally, RBFE calculations, even though cheaper than ABFE calculations, are still notably time-consuming, often taking several hours even with state-of-the-art GPUs. This computational cost underlines the significance of well-planned FE network calculation strategies, which can dramatically enhance the efficiency of building a candidate ranking. Generally, there is a trade-off to be made between, on the one hand, a minimal number of required calculations (∆∆G BA bind s to be calculated) and, on the other, added redundancy in order to increase robustness and gain enhanced insights into the statistics of the final results via thermodynamic cycle analysis. Several approaches have been described to find good calculation strategies/networks. Some examples were directly borrowed from graph theory, such as the minimal spanning tree (MST) and star networks. More sophisticated approaches have also been suggested.