Needle-in-a-haystack problems, in which the optimum value appears in a small portion of the total search space, exist across a wide range of fields including disease prediction and materials property optimisation. The problems typically pose a challenge to Bayesian optimisation algorithms which exhibit slow convergence or get stuck in local optima. Siemenn et al. developed a new approach they termed the Zooming Memory-Based Initialization algorithm (ZoMBI) to tackle Needle-in-a-haystack problems building on traditional Bayesian optimisation. Their approach starts by iteratively zooming in on the manifold search bounds, with each dimension handled independently, using a set number of memory points to identify the plausible region containing the global optimum needle. Next, the memory points not being used to zoom in the search bounds are pruned to reduce the computational cost. In an approach that better mimics the human learning process, actively learned acquisition function hyperparameters are used to tune the preference between exploration and exploitation.