Molecular generation and property prediction have traditionally relied on models with strict equivariance, particularly preserving E(3) symmetries through equivariant models. However, recent advances in large-scale models suggest that unconstrained architectures, trained on extensive datasets, can implicitly learn these symmetries. In this work, we explore whether strict equivariance is required and present \texttt{Rapidash}, an architecture that allows breaking exact equivariance constraints. Using this architecture, we achieve state-of-the-art performance in molecular generation and property prediction, surpassing traditional equivariant models.