In this work we compared four different transfer learning approaches: fine-tuning, multi-task learning, mixed fine-tuning and ensemble decoding. Our aim was to improve the performance of seq2seq retrosynthesis prediction models for ring-breaking disconnections. We have found that mixed fine-tuning performs best overall, with short training time, top-1 reactant-accuracy We then showcased the practical utility of the mixed fine-tuned model by using it for multi-step retrosynthesis of two newly-discovered, complex drug-like compounds containing heterocycles. This illustrates how the model can be used to assist synthetic and medicinal chemists, aiding them in designing synthetic routes towards novel heterocycle-containing therapeutics.