Automatically detecting sound events with Artificial Intelligence (AI) has become increas- ingly popular in the field of bioacoustics, ecoacoustics, and soundscape ecology, particularly for wildlife monitoring and conservation. Conventional methods predominantly employ supervised learning techniques that depend on substantial amounts of manually annotated bioacoustic data. However, manual annotation in bioacoustics is tremendously resource- intensive in terms of both human labor and financial resources, and it requires considerable domain expertise. Moreover, the supervised learning framework limits the application scope to predefined categories within a closed setting. The recent advent of Multi-Modal Language Models has markedly enhanced the versatility and possibilities within the realm of AI appli- cations, as this technique addresses many of the challenges that inhibit the deployment of AI in real-world applications. In this paper, we explore the potential of Multi-Modal Language Models in the context of bioacoustics through a case study. We aim to showcase the potential and limitations of Multi-Modal Language Models in bioacoustic applications. In our case study, we applied an Audio-Language Model–—a type of Multi-Modal Language Model that aligns language with audio / sound recording data—–named CLAP (Contrastive Language–Audio Pretraining) to eight bioacoustic benchmarks covering a wide variety of sounds previously unfamiliar to the model. We demonstrate that CLAP, after simple prompt engineering, can effectively recognize group-level categories such as birds, frogs, and whales across the benchmarks without the need for specific model fine-tuning or additional training, achieving a zero-shot transfer recognition performance comparable to supervised learning baselines. Moreover, we show that CLAP has the potential to perform tasks previously unattainable with supervised bioacoustic approaches, such as estimating relative distances and discovering unknown animal species. On the other hand, we also identify limitations of CLAP, such as the model’s inability to recognize fine-grained species-level categories and the reliance on manually engineered text prompts in real-world applications.