Assessing whether a molecule can be synthesised is a primary task in drug discovery. It enables computational chemists to filter for viable compoundsor bias molecular generative models. The notion of synthesisability is dynamic as it evolves depending on the availability of key compounds. A common approach in drug discovery involves exploring the chemical space surrounding synthetically-accessibleintermediates. This strategy improves the synthesisability of the derived molecules due to the availability of key intermediates.Existing synthesisability scoring methods such as SAScore, SCScore and RAScore, cannot condition on intermediates dynamically.Our approach, Leap, is a GPT-2 model trained on the depth, or longest linear path, of predicted synthesis routes that allows information on the availability of key intermediates to be included at inference time.We show that Leap surpasses all other scoring methods by at least 5% on AUC score when identifying synthesisable molecules, and can successfully adapt predicted scores when presented with a relevant intermediate compound.