We used four separate datasets for this study. The first is a set of azaphenalenes previously studied by some of us, comprising 256 substituted compounds, for which excitation energies were computed with (2nd-order) algebraic-diagrammatic construction, ADC(2)/cc-pVDZ, and geometries were optimized with B3LYP/cc-pVDZ. The second is a set of 138 substituted azaazulenes with ADC(2)/cc-pVDZ excitation energies and optimized with the B97-3c composite method. The third is a set of 16 rationally designed scaffolds, which including substitutions amounts to 68695 unique compounds, optimized at the B97-3c level and with excitation energies at the ADC(2)/cc-pVDZ level. The fourth is a set 315 non-alternant hydrocarbons including substitutions, divided into three subsets of size 76, 187 and 52, optimized at the 𝜔B97X-D/def2-TZVP level and with excitation energies at the CC2/aug-cc-pVTZ level. Below, we compare our PPP-based method to the reference levels at the DFT-optimized geometries given in the datasets. For the azaphenalene dataset, we also investigated the impact of geometry. An initial geometry of each molecule was generated with the EmbedMolecule function in RDKit, 60 and then optimized with MMFF94. This geometry was then further refined with either the GFN-FF force field, 62 the GFN2-xTB semi-empirical method, or the ANI-1ccx machine learning potential (as implemented in TorchANI 65 and using ASE as the optimization backend 66 ).