  Composing Graph Neural Networks (GNNs) of operations that respect physical
symmetries has been suggested to give better model performance with a smaller
number of learnable parameters. However, real-world applications, such as in
high energy physics have not born this out. We present the novel architecture
VecNet that combines both symmetry-respecting and unconstrained operations to
study and tune the degree of physics-informed GNNs. We introduce a novel
metric, the \textit{ant factor}, to quantify the resource-efficiency of each
configuration in the search-space. We find that a generalized architecture such
as ours can deliver optimal performance in resource-constrained applications.
