Grid-based, conformer-averaged (GBCA) indicator field descriptors, such as the average steric occupancy descriptor (ASO) and the average electronic indicator field (AEIF), were useful in the enantioselectivity prediction workflow developed in this laboratory. A naïve implementation of the GBCA descriptors suffers from significant, unfavorable scaling dependencies with respect to the grid size. This step was very computationally expensive to carry out on libraries of tens of thousands of molecules, requiring high performance computational hardware. To eliminate the slow process of descriptor computation, we performed an optimization. Molli employs two levels of optimization of the computing process. The optimization of the GBCA descriptors began by outsourcing numerically intensive arrayed calculations to a more efficient C implementation of the numpy package (Table ). A 25-40-fold acceleration was observed; however, the processing time was still high for libraries of >1M conformers. Thus, an auxiliary C++ sublibrary (called molli_xt) was created through the use of pybind11. Two functions were implemented that reproduced the behavior of SciPy's cdist function that computes the distance matrix (and an analogous function was made that would compute a higher dimensional analog of the distance tensor). This process provided a considerable speed enhancement owing to elimination of slower Python code overhead. In 64-bit floats, molli achieved 1.96±0.05 acceleration by elimination of the extra for-loop in the distance matrix computation. A further 1.34±0.04 fold increase in performance was gained by computing the distance matrix in 32-bit floats, giving a total acceleration of 2.62±0.08. Relative errors in squared Euclidean distance did not exceed 2×10 , and the resulting ASO mean absolute errors were less than 2×10 -9 for 99% of the data (see the Supporting Information for details). For a selected small number of samples, this error was larger because of cases wherein grid points were located close to the van der Waals boundary. To further reduce the size of the problem, the grid was pruned to eliminate the points that lie far away from any atoms for which the values could be assigned as zeros (see Table grid sparsity). To enable this process, we employed the SciPy implementation of the k-d tree data structure. Pruning the grid for ASO computations reduced the grid size by 80 to 90%, therefore providing an average of 5-fold acceleration. Overall, combining these optimizations achieved a 1,700× acceleration of the process compared to a naïve python implementation, and a 50× acceleration as compared to naïve numpy approach.