Applications of computational methods to predict binding affinities for protein/drug complexes are routinely used in structure-based drug discovery. Applications of these methods often rely on empirical force fields (FFs) and their associated parameter sets and atom types. However, it is widely accepted that FFs cannot accurately cover the entire chemical space of drug-like molecules, due to the restrictive cost of parametrization and the poor transferability of existing parameters. To address these limitations, initiatives have been carried out to develop more transferable methods, in order to allow for more rigorous descriptions of any drug-like molecule. We have previously reported H-TEQ, a method which does not rely on atom types and incorporates well established chemical principles to assign parameters to organic molecules. The previous implementation of H-TEQ (a torsional barrier prediction method) only covered saturated and lone pair containing molecules; here, we report our efforts to incorporate conjugated systems into our model. The next step was the evaluation of the introduction of unsaturations. The developed model (H-TEQ3.0) has been validated on a wide variety of molecules containing heteroaromatic groups, alkyls, and fused ring systems. Our method performs on par with one of the most commonly used FFs (GAFF2), without relying on atom types or any prior parametrization.