  Gadget3 is nowadays one of the most frequently used high performing parallel
codes for cosmological hydrodynamical simulations. Recent analyses have shown
t\ hat the Neighbour Search process of Gadget3 is one of the most
time-consuming parts. Thus, a considerable speedup can be expected from
improvements of the u\ nderlying algorithms. In this work we propose a novel
approach for speeding up the Neighbour Search which takes advantage of the
space-filling-curve particle ordering. Instead of performing Neighbour Search
for all particles individually, nearby active particles can be grouped and one
single Neighbour Search can be performed to obta\ in a common superset of
neighbours. Thus, with this approach we reduce the number of searches. On the
other hand, tree walks are performed within a larger searching radius. There is
an optimal size of grouping that maximize the speedup, which we found by
numerical experiments. We tested the algorithm within the boxes of the
Magneticum project. As a result we obtained a speedup of $1.65$ in the Density
and of $1.30$ in the Hydrodynamics computation, respectively, and a total
speedup of $1.34.$
