In this work, we integrate our previously reported AL strategy and well-tempered metadynamics (WTMetaD) to create an automated workflow that reduces the computational cost associated with dataset generation while ensuring sufficient sampling, thereby eliminating the need for prior AIMD simulations. Furthermore, we extend our strategy to include inherited bias well-tempered metadynamics (WTMetaD-IB), allowing us to carry 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; 53 however, it directly incorporates the AL framework to enable the training of MLIP for flexible systems.