The Tartarus benchmarking suite includes tasks aimed at enhancing the efficiency of organic solar cells by evaluating single point GFN2-xTB calculations 94 of candidate molecules. These tasks focus on identifying small organic donor molecules with optimal power conversion efficiency for use in bulk heterojunction devices. Specifically, we selected four tasks for this study: maximising HOMO-LUMO gap values, minimising LUMO energy values, maximising the molecular dipole moment and maximising a combined score defined as the molecular dipole moment plus the HOMO-LUMO gap minus the LUMO energy. This combined score approximates the power conversion efficiency of the proposed molecules. For ease of evaluation, we make use of a surrogate deep learning model, delivered with Tartarus, which was trained on a subset of approximately 25,000 molecules sampled from the Harvard Clean Energy Project Database to predict the optimisation values for these tasks.