We recently developed a set of steric molecular descriptors tailored for dirhodium paddlewheel catalysts. These are privileged catalyst scaffolds with large, conical reactive pockets, that provide a confined environment conducive to selective transformations. As a result of these complex 3D-conformations, steric features of these catalysts cannot be adequately parametrized using traditional molecular descriptors. For instance, Sterimol descriptors are highly dependent upon the selection of the L-axis. This can be difficult to apply to systems with multiple bridging ligands and distinct axial binding sites (Figure ). Similarly, VBur assumes a spherical binding environment around the metal center of interest, and the radius of search space is often too small to encompass the distal ligand environment (Figure ). As a result, these steric descriptors that were designed for small molecule catalysts were found to be insufficient to describe the complex cavity environments in dirhodium catalysts. Free and open-source toolkits for assessing the size and shape of catalytically active pockets are well established and utilized in fields such as protein docking. These toolkits also have limitations that prevent flexible application to a diverse set of structures. First, many of these programs do not allow for pocket analysis of structures containing subunits beyond the scope of amino acids or DNAbases, and thus cannot be applied to many small molecule transition metal catalysts. Second, these programs are designed to analyze pockets encompassed within or between larger molecules and can struggle to provide a reasonable cut off for pockets with a wide entry. Finally, most methods interpretate pocket accessibility on the basis of solvent access. This method typically relies on generating a "space filling" 22 model of points with assigned Van der Waals radii to parametrize an active cavity through its interactions with solvent models. Other approaches for pocket description have been explored, including generating representations based on ligand docking, electron density maps, grid-based approaches , and machine learning algorithms . These established methods can still overestimate the size and accessibility of specific regions within a pocket from the perspective of a bound molecule. Approaches based on experimental assessment of a series of docked molecules can inherently limit the domain of applicability of the pocket information to structurally similar molecules. Thus, a general method to generate quantitative pocket representations remain of interest.