Despite their potential, the practical relevance of chemical language models in drug discovery remains limited, with only a few prospective applications reported thus far. This limitation may be attributed to the inadequate consideration of the synthesizability of generated compounds, which is an essential factor for practical drug discovery. While the physicochemical properties and predicted bioactivities of these compounds have been comprehensively studied, their synthesizability has often been overlooked due to the difficulty in accurately quantifying this property.