Our computed Δ𝐸 ST with the linearly corrected CIS + DSP show fair correlations with the CC2 values, with 𝑅 2 s of 0.68, 0.69 and 0.56, respectively (Figure ). Gratifyingly, the recalls are 0.50, 0.80 and 0.74, respectively, showing that we can recover a large part of the inverted molecules found with the much more expensive CC2 method (Table ). Although some of the compounds are also present in the rationally designed set that we used for the linear correction above, we believe that they are not sufficiently many to compromise the use of the Garner data as external validation (Figure ). We also indicate in the plots which compounds show instabilities (Figure ). There seems to be no clear deterioration in the performance, although also the reference data might exhibit instabilities as there is no mention of any stability analysis in the original manuscript. In summary, the external validation shows that the PPP method can recover inverted molecules at a fraction of the cost of more expensive methods such as CC2. Effect of geometry Even though our PPP protocol is very fast, this speed would not be beneficial if DFT-optimized geometries were required to accurately reproduce the energy gaps. In the benchmarking above, we used the same geometries as in the original datasets to allow for a comparison based on equal footing. For ultrafast screening, we would need geometries from force fields, semi-empirical methods or machine learning potentials that can be obtained on a similar time scale as the PPP results. We chose the azaphenalene dataset for a limited benchmark, and optimized the geometries with the MMFF94 (average runtime of 11.45 ms) and GFN-FF (40.18 ms) force field methods, the GFN2-xTB semi-empirical method (228.13 ms) and the ANI-1ccx machine learning potential (572.83 ms, CPU-based). While the two force fields methods are insufficiently accurate, both GFN2-xTB and ANI-1ccx provide sufficiently good geometries for screening (Figure , Figure and Table ). In particular, GFN2-xTB shows good correlation (𝑅 2 = 0.94) with the gaps at the DFT-optimized geometries. Compared to the reference ADC(2)/cc-pVDZ level, the results with GFN2-xTB geometries are equally good to those from DFT geometries (𝑅 2 of 0.80 vs. 0.81 and recall of 0.57 vs. 0.56, respectively). The optimization runtime of 228.13 ms is slower, but of a similar magnitude compared to the runtime of the PPP protocol itself (47.05 ms), and significantly faster than DFT optimization. Based on the good performance, it is likely that the GFN2-xTB geometries could be used in virtual screening campaigns.