• For fully-continuous functions, Atlas uses a constrained gradient-based acquisition optimization strategy based on the SLSQP algorithm. For highly constrained problems, we observe that SLSQP undergoes serious convergence issues, significantly increasing the per-iteration runtime of Atlas. Atlas' genetic acquisition function optimizer is much faster and does not sacrifice performance compared to SLSQP (comparative tests detailed in SI Sec. S.1 A 2). Thus, we use the genetic acquisition optimizer for all tests in this work.