Large language models (LLMs) excel at few-shot learning, but their ability to reject out-of-distribution examples remains under-explored. We study this challenge under the setting of few-shot open-set classification, where a model must not only classify examples from a small set of seen classes but also reject unseen ones at inference time. This setting is more realistic and challenging than traditional closed-set supervised learning, requiring both fine-grained classification and robust rejection. We show that, for small LLMs, neither chain-of-thought (CoT) prompting nor supervised fine-tuning (SFT) alone are sufficient to generalise reliably, particularly when class semantics are anonymised. We introduce Wasserstein GFN (W-GFN), a novel amortised Generative Flow Network approach that uses latent trajectories to approximate the Bayesian posterior. With as few as 4 examples per class, W-GFN substantially improves performance, enabling LLaMA 3.2B to achieve geq 80% of the performance of LLaMA 3.3 70B in complex datasets, despite being sim 23 times smaller which highlights the importance of reasoning-aware approaches for robust open-set few-shot learning.