Due to the exacting accuracy required, a primary task in organic CSP is correcting lower level landscapes to higher levels of theory. This may also include re-optimization of the geometries of predicted structures. However, single point corrections are also common, where the geometries not updated when energies are re-evaluated at the higher level. The latter correction is especially relevant for MLPs trained by ∆-learning from CSP landscapes. To investigate this application, we applied our active learning workflow detailed in the previous section to the CSP landscapes of two challenging systems -TTBI and resorcinol -and then used the resulting cNNPs to generate corrected landscapes.