The quantitative evaluation of a given mapping remains a critical question in free energy calculations, beyond the mapping phase. The number of potential mappings for a set of N ligands is N (N -1)/2. 32 However, to generate a comprehensive ligand ranking, only a set of N -1 mappings is necessary. An efficient network with N -1 mappings can be constructed with the minimal spanning tree (MST) approach, which ideally uses the best N -1 mappings of all possible mappings. Additional edges are often incorporated into perturbation networks to enhance the robustness of the free energy estimates. This strategy is evident in cycle closure approaches, for instance. To automatically obtain a quality assessment of mappings, various scoring approaches have been proposed in the past, such as Lomap and machined learned approaches by Scheen et al.. In this study, we investigate geometry-focused metrics to analyze the mappings, which could be of potential interest to the development of mapping scoring functions, like the mappings-based root mean squared deviation (RMSD), the mapped volume ratio (MVR), the mapped atom ratio (MAR), the shape mismatch score (SMS) and the shape overlap score (SOS).