In this publication, an overview on how ArchOnML operates will be provided alongside an example virtual screening for excited state properties of more than 1.3 million derivatives of anthraquinone (AQ). The following Methodology section will outline what tasks ArchOnML carries out and how the package was designed at release. After that, the Results and Discussion section will showcase the performance of the currently implemented Kernel Ridge Regression (KRR) model and provide a basic analysis of the screening results. Finally, the Conclusion section will provide a discussion on the accuracy of ML-assisted screening methods, the speed-up of the proposed workflow and the implications of formulating purely data-driven design rules.