In this research, we investigated the potential of hyper-parameter tuning, alternative model architectures, and knowledge distillation (KD) to accelerate both single-step and multi-step retrosynthesis prediction. The initial motivation was to reduce model size in a system that makes multiple model queries, hypothesizing that speed increases in a single-step setting would have a compounding effect in a multi-step setting. While KD offered promising speed improvements, these gains were often accompanied by reduced accuracy, and the effectiveness of KD varied significantly across different model architectures. This suggests that benefits of KD are architecture-dependent and that careful tuning is required to avoid undesirable trade-offs. Further investigations into multi-speed settings uncovered important aspects of single-step retrosynthesis models which impact multi-step, including top-10 accuracy influence on solvability and the influence of exploration on multi-step speed. Future research into retrosynthesis prediction should focus on optimizing the exploration/exploitation dynamics, particularly in multi-step scenarios, and further refine KD techniques, alternative architectures and hyper-parameter optimizations to balance speed and accuracy. Investigating hybrid methods that combine the strengths of KD with advanced model architectures may also prove useful in improving both the efficiency and robustness of retrosynthesis predictions. Although reducing model size and improving single-step speed are important, our results indicate that multi-step search efficiency is more significantly influenced by the diversity and confidence of single-step models. In Monte Carlo-based (MC) multi-step retrosynthesis, these factors play a crucial role in balancing exploration and exploitation during the search process, often outweighing the direct impact of single-step model speed.