The paper discusses improving neural-network quantum states (NQS) training via Variational Monte Carlo (VMC) methods. The authors propose a systematic strategy to tackle sampling issues by means of adaptively tuned importance sampling that can reduce the computational cost of vanilla VMC considerably, up to a factor of 100x when targeting highly peaked quantum chemistry wavefunctions.