Structure-based ML models. Having established an efficient conformer search workflow, we now turn to structure based ML models for predicting λ (K s ). As these model require 3D geometries as inputs, they are well suited to investigate the effect of the conformer search protocols on the ML models themselves, see Fig 3 . Here, learning curves for λ GFN1 and λ DFT are shown. While all models improve with more data, two striking differences can be seen. First, the models using the more accurate conformer search conf3 are consistently better than the ones using conf2. Second, the predictive error is consistently lower for λ GFN1 than for λ DFT .