During the sampling process, the probability ratios of the expert and anti-expert models serve as soft constraints on the prior model. These constraints guide the sampling process toward promising regions within the target chemical space by rejecting tokens that are likely to lead to compounds with undesirable properties. Notably, by taking the ratio of predicted probabilities, the implicit biases shared by both the expert and anti-expert models are canceled out, thereby mitigating unintended side effects. This makes the PoE chemical language model well-suited for drug discovery applications, which require the simultaneous optimization of multiple molecular properties utilizing data from various sources.