  We study the problem of learning conditional distributions of the form $p(G |
\hat G)$, where $G$ and $\hat G$ are two 3D graphs, using continuous
normalizing flows. We derive a semi-equivariance condition on the flow which
ensures that conditional invariance to rigid motions holds. We demonstrate the
effectiveness of the technique in the molecular setting of receptor-aware
ligand generation.
