This survey is one of the first in literature to present a comprehensive review on available Large Language Models (LLMs) in the Drug Discovery (DD) domain. It first differentiated the various tasks they serve and classified the models based on that. Then, it investigated their approaches, methodologies, performances, and the uniqueness of each model. Following that, there was a comparative analysis on their architecture, used training datasets and their size, augmented parameters, performance evaluation metrics, and the availability of source code and data. This survey is an evident that LLMs are of great potential to further advance the DD process and yield more efficient results. However, we must be aware that the scope is large and there are many areas of influence that can be contributed to in future work, with a superior advantage for models that succeed to integrate multiple parameters, resulting with more reliable predictions.