At present, recently developed ML methods for the calculation of atom charges are less computationally intensive and have accuracy comparable to that of DFT calculations. For example, Rathi et al. developed a deep neural network (DNN) model to fast generate off-center atomic charges for ESP surfaces calculation, and soon afterwards, Derek P. Metcalf and coworkers used an electron-passing neural network to predict atomic charges while total molecular charge conservation is guaranteed. Most recently, Thürlemann et al. used graph neural networks (GNN) to generate atomic multipoles for ESP calculation. Their GNN model was demonstrated to be accurate and effective on a diverse range of systems. With these ML models in hand, we can quickly obtain QM-level ESPs for both ligands and proteins. Because of the importance of ESP, researchers call for greater attention for the utilization of EC in drug design. A favorable indicator of the binding affinity between a ligand and its target can be seen through the complementarity of the ESP values. Therefore, it is essential to develop an index or score to quantify EC. The relationship between EC score and activity was demonstrated in some studies. Bauer and Mackey reviewed the application of EC to describe how well the ligand and protein matches in electrostatic interaction, developing a formula to calculate an EC score for local complementary measurement. Integration of the local EC scores into their total EC score function shows moderately improved relationship with bioactivity. The EC score function they published, however, is not strictly in range [-1, 1]. As far as we know, how to define the linear relationship between EC scores and bioactivity is thus far unclear, and EC scores as an effective indicator for ligand design in some high electrostatic interaction systems is underdeveloped with limited success. In order to extend EC application and improve its scoring function, we developed a piecewise function as the local EC score with consideration of physics context. We modified the EC score function proposed by Bauer and Mackey and achieved an improved performance in comparison to the traditional Pearson!s coefficient. In addition, we propose two indices to facilitate automatic selection of ligands suitable for EC score-based screening.