The combination of AL with enhanced sampling methods, such as umbrella sampling (US), metadynamics, and their variants, shows promise for building datasets more efficiently. Uncertainty-driven enhanced sampling techniques have been applied to train MLIPs for various systems, such as alloys and polymers, glycine and proton transfer in acetylacetone, and alanine dipeptide and metal-organic frameworks. These techniques improve the accuracy and stability of the generated MLIPs by sampling regions of the configuration space with large uncertainty in predicted properties. However, their robustness on reactive systems is dependent on the selection of bias parameters. Several reactive MLIPs have been developed to model chemical reactions integrating metadynamics with AL. These include reactions in the gas phase, such as Diels-Alder reaction, and explicit solvent (urea decomposition in water, oxygen reduction at Auwater interfaces, ring opening of N-enoxyphthalimide, S N 2 reaction, and a Menshutkin reaction ). However, many of these strategies often require preliminary exploration of the relevant PES by AIMD. Such high input computational costs still limit their applicability to larger systems.