Noticing that the physically-motivated EDA-and ML-based approaches possess advantages that are quite complementary to each other, we aim to build a purely distance-based range-separated water model at CCSD(T) level of theory in this work. Following the ideas of the previous work, we employ the TD-DFT and SAPT based methods to develop accurate long-range components, in conjunction with a shortrange neural network (NN) potential. We will test the extendibility of the model by investigating its performances on both water clusters of different sizes and bulk simulations. Through this work, we demonstrate that a really careful treatment to the long-range potential is crucial to boost both the computational efficiency and the transferability of the ML model. Compared to existing models, the MBEfree range-separation scheme and the SAPT-based long-range components utilized in this work are easier to be generalized to complicated multicomponent systems. Therefore, the current work lays a solid foundation for the future development of a general-purpose organic molecular force field at CW level of theory.