Here, we introduce a new workflow that combines transfer learning (TL) with active learning (AL) strategy 55-57 -we first learn the PES from existing high-quality physics-based models in literature which describes the bulk systems efficiently, and subsequently augment this learning via retraining with a higher fidelity albeit sparsely sampled first-principles training dataset to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to transfer the learning from decades of high-quality bulk interatomic potentials and subsequently improve via learning from a sparsely sampled dataset that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly non-equilibrium. Our workflow deploys transfer and active learning strategies that iteratively improves the fingerprinting depending on model fidelity. It allows us to train NN that learns the description of bulk and nanoscale potential energy surface from minimal amount of first-principles training data which is sampled on-the-fly from Nested Ensemble Monte Carlo simulations. Briefly, we introduce a two prong strategy (i) active learn the pair-wise interactions from best spherically symmetric potential models (ii) improve the learning by introducing 3-body and 4-body interactions against an additional first-principles sampled dataset. In active learning level, we start with the a minimum amount of training data (∼ 1 to 3 data points) which are continuously updated using data points sampled with a Nested Ensemble Monte Carlo scheme that iteratively queries energy landscape and retrains the network with an updated training set that includes failed configurations/energies from previous iteration until convergence is attained. The final training set at the time of convergence is used as starting dataset in the next level of training.