In this work, we integrate our AL strategy and well-tempered metadynamics (WT-MetaD) to establish an automated workflow that reduces the computational cost associated with dataset generation while ensuring sufficient sampling, removing the need for prior AIMD simulations. Furthermore, we extend our strategy to include inherited bias well-tempered metadynamics (WTMetaD-IB), allowing us to bring forward the accumulated bias from previous AL iterations, further increasing the efficiency of the training process. This approach parallels the recently published incremental learning scheme applied in metal-organic frameworks, however, with direct incorporation of the AL framework to enable training of MLP for flexible systems. We illustrate the performance of the WTMetaD-IB AL approach using linear Atomic Cluster Expansion (ACE) potentials to model three organic reactions, namely the S N 2 reaction between fluoride and chloromethane in implicit water (R1), the methyl shift of 2,2-dimethylisoindene in the gas phase (R2), and the glycosylation reaction between α-trichloroacetate and i -PrOH in explicit dichloromethane (DCM) solvent (R3). For the first two examples, accuracy and sampling efficiency are compared to our previously reported AL+ downhill dynamics strategy. We demonstrate that integration of WTMetaD-IB and AL results in accurate and data-efficient MLPs, without a priori knowledge of the relevant TSs and/or reaction pathways. The accuracy and stability of the MLP trained using WTMetaD-IB AL demonstrate the broad applicability of the proposed method with reduced human effort, facilitating the broader use of MLPs in modelling reactivity.