Together, these procedures enable the optimization method to locate the region containing the optimum needle and avoid local optima. The method was benchmarked on two real-world problems, namely identifying materials with a highly negative Poisson's ratio and materials with a highly positive thermoelectric figure of merit, for which exhaustive computational datasets were available. They find the ZoMBI algorithm outperforms standard Bayesian optimisation (a computational speed up of 400×) and other state-of-the-art algorithms designed for need-in-a-haystack problems (3× fewer experiments).