We present an approach for unsupervised domain adaptation—with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift—from a class-conditioned domain alignment perspective. Current methods for class-conditioned domain alignment aim to explicitly minimize a loss function based on pseudo-label estimations of the target domain. However, these methods suffer from pseudo-label bias in the form of error accumulation. We propose a method that removes the need for explicit optimization of model parameters from pseudo-labels directly. Instead, we present a sampling-based implicit alignment approach where the sample selection procedure is implicitly guided by the pseudo-labels. Theoretical analysis shows that implicit alignment facilitates adversarial domain-invariant representation learning. Empirical results and ablation studies confirm the effectiveness of the proposed approach. In particular, our method exhibits superior robustness in the presence of extreme within-domain class imbalance and between-domain class distribution shift.