  We revisit the effectiveness of topological descriptors for molecular graph
classification and design a simple, yet strong baseline. We demonstrate that a
simple approach to feature engineering - employing histogram aggregation of
edge descriptors and one-hot encoding for atomic numbers and bond types - when
combined with a Random Forest classifier, can establish a strong baseline for
Graph Neural Networks (GNNs). The novel algorithm, Molecular Topological
Profile (MOLTOP), integrates Edge Betweenness Centrality, Adjusted Rand Index
and SCAN Structural Similarity score. This approach proves to be remarkably
competitive when compared to modern GNNs, while also being simple, fast,
low-variance and hyperparameter-free. Our approach is rigorously tested on
MoleculeNet datasets using fair evaluation protocol provided by Open Graph
Benchmark. We additionally show out-of-domain generation capabilities on
peptide classification task from Long Range Graph Benchmark. The evaluations
across eleven benchmark datasets reveal MOLTOP's strong discriminative
capabilities, surpassing the $1$-WL test and even $3$-WL test for some classes
of graphs. Our conclusion is that descriptor-based baselines, such as the one
we propose, are still crucial for accurately assessing advancements in the GNN
domain.
