The GOFEE method allows to reduce the number of energy and force evaluations carried out at the DFT level by training, on-the-fly, a surrogate machine-learning energy model. This model is used to carry out local relaxations of every evaluated candidate and to guide the exploration of conformational space, which supposes a considerable improvement in computational efficiency with respect to other algorithms. The GOFEE code therefore relies o a selected number of single point calculations at the DFT level, circumventing the need for carrying out more demanding local relaxations at costly levels of theory.