Antibody drug discovery campaigns often leverage immune repertoires from antigen-exposed animals, which can be divided into clonotypes, subclasses of sequences derived from the same progenitor B-cell. In this work, we adapt discrete walk-jump sampling (dWJS) to condition generation on categorical variables like clonotype, extending both energy-based and score-based dWJS with predictor-free guidance during Langevin dynamics ("walking") and denoising ("jumping"). Categorical and numerical variables are learned during training and specified during sampling, producing diverse and novel sequences from target clonotype classes. We train conditional WJS models on datasets of over 1.5M sequences obtained from antigen-exposed rats and human patients post-vaccination. Surprisingly, increasing guidance improves both sample quality and sequence diversity, enabling controllable sampling from thousands of distinct modes.