Machine-learning interatomic potentials (MLIPs) have emerged as efficient surrogate models for approximating ab initio potential energy surfaces. However, computational searches employing MLIPs are constrained by their reduced accuracy, particularly for out-ofsample predictions of energies and forces on unseen regions of composition or configuration space. Tran et al. developed a multi-fidelity machine learning framework to fuse a hierarchy of atomistic computational models, with density functional theory calculations and a SNAP MLIP 69 used for high-and low-fidelity predictions, respectively. By coupling their model to a Bayesian optimisation procedure, they performed an on-the-fly search for materials with high bulk modulus in the Al-Nb-Ti ternary composition space. Their approach was able to locate the optimum material after only 5 high-fidelity and 31 low-fidelity evaluations, demonstrating the power of multi-fidelity inference coupled with Bayesian optimisation.