Here, we extend the GNN-based implicit solvent (GNNIS) model to 39 common organic solvents and demonstrate how the approach can be employed to rapidly identify Boltzmann-weighted conformational ensembles of small molecules in these solvents. To encode the different solvents in the GNN, we draw on recent developments in the field of large language models (LLMs) and use an embedding scheme that LLMs use to encode words . Our approach allows for sets of random structures (e.g., from a conformer generator) to be minimized into Boltzmann-weighted conformational ensembles that are on par with explicit-solvent simulations. The approach is tested using ∼200 experimentally determined ensembles derived from new NMR measurements as well as literature data. These tests show high accuracy with minimal computational effort and predict solvent-specific differences within one k B T of experimental results. This high speed (e.g., a small molecule's conformational ensemble can be obtained in minutes rather than days) allows the study of large datasets as well as iterative design procedures.