In this study, we present a new intermolecular potential for water based on a hybrid physicallymotivated and data-driven ML methodology. The long-range terms (including electrostatic, polarization, and dispersion) is treated using physically-motivated method, while ML approach is employed to describe the anisotropic and many-body short-range components. We show that by carefully separating all the longrange components, we can use a very small cutoff for our ML potential and enhance its performance. The resulting force field shows not only good accuracy, but also excellent transferability with respect to increasing system sizes, giving excellent performance from clusters to bulk simulations. Our results show that with a careful range-separation scheme and a robust long-range potential, we can construct an accurate molecular force field from very small ab initio calculations (with system sizes no more than five molecules). This finding is especially important for the general force field development at CCSD(T) level of theory, considering the poor scaling of CCSD(T) calculations. The accuracy of our force field is comparable with the state-of-the-art MB-pol potential, and by avoiding the cumbersome MBE, the approach presented in this work is more applicable to larger molecules. This work symbolizes a successful marriage between the physically-motivated method and the data-driven approach. Through a hybrid methodology, we are able to exploit the advantages of both methods: good transferability of the physicallymotivated force fields and the flexibility of the data-driven models. Therefore, we believe this work represents a solid step towards a general-purpose molecular force field at correlated wavefunction level of accuracy.