  Neural quantum states are a new family of variational ans\"atze for
quantum-many body wave functions with advantageous properties in the
notoriously challenging case of two spatial dimensions. Since their
introduction a wide variety of different network architectures has been
employed to study paradigmatic models in quantum many-body physics with a
particular focus on quantum spin models. Nonetheless, many questions remain
about the effect that the choice of architecture has on the performance on a
given task. In this work, we present a unified comparison of a selection of
popular network architectures and symmetrization schemes employed for ground
state searches of prototypical spin Hamiltonians, namely the two-dimensional
transverse-field Ising model and the J1-J2 model. In the presence of a
non-trivial sign structure of the ground states, we find that the details of
symmetrization crucially influence the performance. We describe this effect in
detail and discuss its consequences, especially for autoregressive models, as
their direct sampling procedure is not compatible with the symmetrization
procedure that we found to be optimal.
