For the 6-D systems, using more than 10 4 points is common. Our uncertain-driven active learning procedure can save points to hundreds while losing very little accuracy, which makes it easier to use high-level electronic structure methods for the full-dimension PES fitting of small to mid-size complexes. On the other hand, attention needs to be paid to the unexpected results of the MLRNet. We discussed some possible problems, and suggested introducing an uncertainty for MLR parameter functions in the future. Developing new sampling methods for those "NN + physical function" models may be promising to improve the point sampling efficiency further.