  Molecular line emission is a powerful probe of the physical conditions of
astrophysical objects but can be complex to model, and it is often unclear
which transitions would be the best targets for observers who wish to constrain
a given parameter. We therefore produce a list of molecular species for which
the gas history can be ignored, removing a major modelling complexity. We then
determine the best of these species to observe when attempting to constrain
various physical parameters. To achieve this, we use a large set of chemical
models with different chemical histories to determine which species have
abundances at 1 MYr that are insensitive to the initial conditions. We then use
radiative transfer modelling to produce the intensity of every transition of
these molecules. We finally compute the mutual information between the physical
parameters and all transitions and transition ratios in order to rank their
usefulness in determining the value of a given parameter.
  We find 48 species that are insensitive to the chemical history of the gas,
23 of which have collisional data available. We produce a ranked list of all
the transitions and ratios of these species using their mutual information with
various gas properties. We show mutual information is an adequate measure of
how well a transition can constrain a physical parameter by recovering known
probes and demonstrating that random forest regression models become more
accurate predictors when high-scoring features are included. Therefore, this
list can be used to select target transitions for observations in order to
maximize knowledge about those physical parameters.
