In this work, the ArchOnML python package is introduced and used to perform a chemical space screening of anthraquinone (AQ), applying a Kernel Ridge Regression In the AQ example, the models for predicting excitation energies achieved mean absolute errors (MAE) of less than 0.1 eV for the four states 𝑆 1 , 𝑆 2 , 𝑇 1 and 𝑇 2 and coefficients of determination (𝑟 2 ) of 0.86, 0.67, 0.93 and 0.83, respectively. When considering the doubled standard deviation of prediction errors during model testing, all test predictions are also roughly within 0.1 eV -which is well within the order of magnitude that excited state calculations with TD-DFT can achieve at best. Considering the oscillator strengths of the 𝑆 1 and 𝑆 2 states, the models are less reliable with 𝑟 2 values of 0.74 and 0.22, respectively. Reasons for the relatively poor performance are likely related to both state impurities in the transition character, as well as an overall unfavorable distribution of oscillator strengths for 𝑆 2 specifically. Since the latter consisted of mostly dark excitations for all training entries, it was rather difficult for KRR to connect the value 𝑓 Osc (𝑆 2 ) = 0.0 for almost all molecules in a meaningful way to the variety of different descriptors. To address such issues, future versions of ArchOnML should offer different ML models such as neural networks and test different data pre-conditioning approaches.