We have demonstrated the utility of smirnoff-plugins by training new, transferable small molecule and water force fields using the recently-proposed double exponential non-bonded potential . The added flexibility of the DE-FF functional form was demonstrated through improved training metrics on pure liquid and binary densities, and enthalpies of mixing, when compared to the state-of-the-art OpenFF Sage LJ-based force field. Additional test set improvements in transfer free energies between aqueous and non-aqueous media were also observed (RMS errors of 0.85 and 1.08 kcal/mol for DE-FF and Sage, respectively), alongside comparable conformational energetics. This early success is particularly remarkable given that this is the first such general DE-FF, whereas LJ-based force fields are building on a long history of parameter optimisation and tuning. While currently modestly slower than the LJ soft-core potential, our GPU implementation of DE-FF in OpenMM offers substantial speed-up compared to previous CPU implementations.