To identify potential excited-state Hund's rule violations, we screen vertical excitation energies in the Fragment-Oriented Materials Design (FORMED) dataset, which is publically available. This database consists of nearly 117,000 curated experimentally-reported organic crystal structures and their associated optical properties computed with Tamm-Dancoffapproximated time-dependent density functional theory (TDA-TDDFT) using ωB97X-D/6-31G(d) on geometries optimized using the GFN2-xTB semiempirical method, Figure , top box. Figure : Schematic of the screening workflow from our previous communication, and the improved screening workflow used in this study. TDA-TDDFT-based methods cannot account for double excitation character, which is essential for predicting Hund's rule violations. Indeed, there are many outliers when we compare the first two steps of our previous dataset, that is, the initial TDA-TDDFT-based ωB97X-D/6-31G(d) on GFN2-xTB geometries on one hand, and the second-order approximate coupled-cluster level (CC2/aug-cc-pVDZ) with structures optimized at the ωB97X-D/def2-TZVP level on the other (Figure ). We identified the second step in our previous screening workflow as a bottleneck (Figure , left). In this work, we implemented an extra step of refinement to improve the flow and efficiency of the screening process. We find that the GFN2-xTB-optimized structures produce only small errors in excitation energies relative to the DFT-optimized structures (Figure ). Although the resource scaling of CC2 can be challenging, it is efficient for studying vertical excitation energies in molecules of 20-40 atoms. As illustrated in the improved workflow in Figure , we therefore screen excitation energies at the CC2/aug-cc-pVDZ//GFN2-xTB level prior to further structural refinement at the DFT level. In this way, we optimize fewer structures with DFT in the following step, which was a bottleneck in the initial screening workflow. b) Benchmark of singlet-triplet gaps computed with cc-pVDZ and aug-cc-pVDZ using CC2 or EOM-CCSD methods.