The performance and efficiency of AL strategies heavily depend on the algorithm used to select suitable structures for retraining. Previous studies have utilised various metrics or selectors to evaluate the performance of MLPs within AL iterations. Probably the most straightforward metric is the direct comparison of energies or forces between the reference (ground-truth) method and the MLP. However, this metric requires QM calculations at every evaluation step, which is only feasible for small systems in the gas phase. Gaussian Process-based methods, such as GAP, commonly use energy uncertainty as a metric, which refers to the variance in predicted energy. However, this property is limited to Bayesian models, as they yield a predictive distribution characterised by its mean and variance. Other models only provide a point prediction for a given structure and are thus unable to utilise this metric. Another approach widely used in NNPs, although not limited to, is queryby-committee, where uncertainty is quantified through the variance of predicted energies or forces among an ensemble of MLPs, referred to as committees. In the case of NNs, multiple potentials are trained with the same architecture but with different initial parameters. This approach provides on-the-fly uncertainty estimation but incurs additional computational costs associated with the training of several NNPs. In all the uncertainty metrics mentioned above, a large variance indicates an under-representation of the corresponding data points and the need to include them in the training set.