In prior work , several iterations of adaptive design (also referred to as sequential learning) consisting of exploratory data analysis were followed by a classification-based approach. For the latter, rather than perform regression and take candidates with the best numerical predictions, solutions were classified based on their likelihood of being "extraordinary" , meaning falling in a top x% of all candidates in terms of performance. This resulted in 13 330 packing simulations and and a maximum packing fraction of 0.826. In this work, we instead focus on a small search budget with no pre-existing training data and carry out concurrency-limited BO to maximize packing fraction using low-fidelity (noisy) packing simulations. These characteristics mimic common materials informatics datasets and tasks. We emphasize that the packing fractions reported in Hall et al. should not be compared directly with the packing fractions reported in this work. This is due to significant differences in how the distributions were parameterized and translated into ParPack simulation input files. See Appendix C and Section S1 for discussion and other content related to the differences. Another significant difference arises from the use of far fewer number of particles in this work (25 000 instead of 1.5 × 10 6 ). See Section S3 for the convergence behavior of volume fraction vs. number of particles which shows an initial steep rise in the mean volume fraction. Additional information related to packing fractions as a function of the number of particles may be found in Vågberg et al. , Tallarek .