Score-based diffusion models have emerged as effective approaches for both conditional and unconditional generation.A new method is proposed to sample from the conditional distribution under arbitrary logical constraints without additional training.The method manipulates the learned score to sample from an un-normalized distribution based on user-defined constraints.The approach shows effectiveness in approximating conditional distributions for tabular data, images, and time series.