Here, the acquisition function schedules only some HiFi calculations near the lowest-energy minimum. Once the contribution of this minimum to free energy is sufficiently described, the algorithm quickly explores and improves the second-lowest energy minimum, thereby also aiming for a generally correct shape of the curve and thereby accurate entropy. Bayesian prediction using a problem-oriented acquisition function therefore on the one hand saves calculations on the LoFi level but also proposes meaningful updates at the HiFi level. This enables more efficient 1DHR treatments and shows a way to reduce the computational effort for multidimensional, i.e. coupled, PESs.