Many atom mapping algorithms use cheminformatics-driven approaches focusing on 2D chemical properties and SIS searches in order to estimate the MCS serving as core region, as seen in Lomap, 32 pmx, 48 fkckombu, FESetup, ProtoCaller, or SMArt. Alternatively, atom mapping approaches could be focused on the 3D geometry of the system. One benefit of such a method is the potential to bypass expensive SISs, which are trying to solve an NP-complete problem. Some atom mapping approaches based on SIS, such as pmx and Lomap, actually combine the 2D cheminformatics and the 3D geometry aspects but focus on the SIS algorithm. From practical considerations, the optimal atom mapper algorithm for a given application might depend on the intention of the modeler. For instance, if a given set of coordinates needs to be preserved as much as possible because they represent a binding mode or a special design idea, then a 3D geometry-focused approach might be the preferred method. But if an optimal overlap of the molecules and their chemical properties is important, cheminformatics-based approaches may be more suitable. In this work, we present the package Kartograf, a package that implements an optimal atom mapping algorithm based on 3D geometric information. The proposed algorithm essentially addresses a bipartite graph matching problem using the Jonker-Volgenant algorithm, which has a computational worst-case complexity of O(n) = N 3 . We will showcase features of our approach through theoretical examples illustrating the potential advantages. Furthermore, we will validate our atom mapper with test datasets for relative hydration and binding free energies. Kartograf is seamlessly integrated into the OpenFE environment, but can alternatively be installed as a standalone package via PyPi or the conda-forge. The source code is readily available under MIT license on GitHub at .