We next investigated applying the active learning workflow to resorcinol, a small organic molecule that has been wellstudied as an example of conformational polymorphism. The initial CSP landscape, which was calculated from a pool of conformations and relaxed at the DFTB-D3 level, contains matches to the observed α and β polymorphs. The molecules in these polymorphs differ conformationally by rotation of one hydroxyl group 180 • , transforming between syn-syn and syn-anti conformations. This conformational flexibility means that, whereas the models trained for oxalic acid and TTBI were effectively intermolecular potentials, correcting the resorcinol landscape requires training a model that describes both intramolecular and intermolecular energy corrections. To add to this, the DFTB-D3 description of the relative energies of the resorcinol structures contains clear deficiencies and correlates poorly with the target PBE-D3 relative energies (Figure ), which increases the difficulty of learning the correction. It is also notable that the DFTB-D3 landscape incorrectly ranks the β polymorph lower in energy than the α polymorph, opposite to the expected order.