The three trained GNN models (DFTNN, ExpNN-dft, and ExpNN-ff) were evaluated using an external dataset of chemical shifts, CHESHIRE, which is widely used to benchmark DFT methods (Fig. ). ExpNN ), however, all are outperformed by our two GNN models augmented by transfer learning against experimental data. Of these, ExpNN-ff is around four orders of magnitude faster. Encouraged by this comparison against DFT methods that have been applied successfully to revise organic structures, we next set out to apply whether the ExpNN-ff model can be accomplish more challenging applications of structure elucidation in seconds. We next investigated the performance of the ExpNN-ff model for organic structures larger than those used for network training (MW > 500). We compared our predicted C chemical shifts against experimental values for 650 large molecules (MW > 500) taken from NMRShiftDB (Fig. ). Each prediction requires at least one MMFF conformation of a given molecule and where multiple conformers were present a Boltzmann-weighted average was used. As an illustrative example, we used ExpNN-ff's predictions to detect obvious database errors/misassignments in an automated, high-throughput fashion. Predicted chemical shifts were first compared against the structural assignments from NMRShiftDB. For structures with MAE values > 3.5 ppm the experimental shift values were reordered to find the optimal assignment (i.e., lowest MAE, Fig. ). One such example automatically identified is shown in Fig. , where enoate aand b-carbon shifts were found to be swapped in the experimental assignment. After this workflow was complete, remaining egregious outliers were then inspected manually. The structure of Taxol C (ID: 20244313) was found to be incorrectly recorded in the database, with a cyclohexyl rather than phenyl ring.