To further increase the accessibility of ML-assisted screening methods, this work introduces ArchOnML ("Archive-On-Machine-Learning"): An open-source package that provides support for the complete workflow of virtual screening -including all steps from generating the chemical space until performing predictions. By applying a modular programming style, the package allows to add and adapt functionality (models, descriptors, tasks) in a straightforward way, encouraging ML developers to contribute to this free open-source software. For the computation of (semi-empirical) quantum chemistry ((S)QC) descriptors and labels, ArchOnML parses inputs for use in external program packages such as Gaussian or Orca. The code package comes with a comprehensive user and developer guide and can be freely downloaded from github at ().