While our method captures the inverted gap of azaphenalene and pentalene, it also needs to capture trends with substitution to work effectively in virtual screening. We therefore calculated 256 substituted azaphenalenes that have previously been studied by some of us. The PPP level S 1 and T 1 excitation energies are well correlated with those from ADC(2) with 𝑅 2 of 0.90 and 0.94, respectively (Figure ). Unfortunately, the oscillator strengths are not as well reproduced with an 𝑅 2 of 0.54, although the Spearman 𝜌 of 0.82 indicates the values might be used to rank potential candidates (Figure ). The problem of obtaining accurate oscillator strengths at the PPP level of theory is well known in the literature, and is especially exacerbated with low oscillator strengths which are prevalent in this dataset. Crucially, the Δ𝐸 ST shows good correlations, with 𝑅 2 = 0.81 using Δ𝐸 DSP ST,CIS (Figure ). This 𝑅 2 value is essentially unchanged from the gap at the CIS level without DSP (0.81, Figure ) and markedly better than for the gap at the SCF level (0.52, Figure ), showing the importance of going beyond the SCF level to include at least some configuration interaction. The results are of similar or better quality recently achieved by Pernal and co-workers for a different set of azaphenalenes using SCF + DSP with orbitals from DFT. To further analyze the results in terms of a binary classification into normal/inverted, we calculated the true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN), as well as a range of common classification scores (Table ). For definitions, see Equation S1 to Equation S6. The most important metrics for virtual screening are, in our opinion, the recall and the specificity. The recall measures the fraction of inverted molecules that the protocol captures, and is 0.58 at the SCF level and 0.56 at the CIS level. The specificity measures the fraction of non-inverted molecules that the protocol identifies, and is 0.94 at the SCF level and 0.97 at the CIS level. These metrics mean that we are able to capture a large proportion of the molecules with inverted gaps, while filtering out most non-inverted molecules. Below we will show how we can improve the results even more with a linear correction to Δ𝐸 DSP ST,CIS based on results from a wider set of compounds. The results for these azaphenalenes indicate that the PPP protocol could be used to prescreen candidates of this compound class for Δ𝐸 ST , while further pruning with another method is likely needed for the oscillator strengths. While we see success for the azaphenalenes, the situation for the azaazulenes is unfortunately worse. The PPP level S 1 and T 1 excitation energies are only moderately correlated with those from ADC(2) with 𝑅 2 of 0.44 and 0.47, respectively (Figure ). For the oscillator strengths, the situation is rather catastrophic with 𝑅 2 of 0.00 implying no correlation whatsoever (Figure ). The situation for the Δ𝐸 ST is also worse than for the azaphenalenes, with 𝑅 2 = 0.46 with CIS + DSP, although a marked improvement over 0.18 with SCF + DSP (Figure ,f). Unfortunately, the negative gaps are not recovered, leading to recalls of 0.00 as none of five inverted molecules could be identified (Table ). It could be speculated that the worse performance for the azaazulenes comes from the fact that they are non-alternant molecules, for which the approximations are expected to be less applicable. Considering the contrasting performance for the azaphenalenes and azaazulenes, it seems clear that the PPP protocol will miss hits for some compound classes. To investigate the global performance of the model we turned to a more diverse set with 16 different compound classes.