  Non--Contact Atomic Force Microscopy with CO--functionalized metal tips
(referred to as HR-AFM) provides access to the internal structure of individual
molecules adsorbed on a surface with totally unprecedented resolution. Previous
works have shown that deep learning (DL) models can retrieve the chemical and
structural information encoded in a 3D stack of constant-height HR--AFM images,
leading to molecular identification. In this work, we overcome their
limitations by using a well-established description of the molecular structure
in terms of topological fingerprints, the 1024--bit Extended Connectivity
Chemical Fingerprints of radius 2 (ECFP4), that were developed for substructure
and similarity searching. ECFPs provide local structural information of the
molecule, each bit correlating with a particular substructure within the
molecule. Our DL model is able to extract this optimized structural descriptor
from the 3D HR--AFM stacks and use it, through virtual screening, to identify
molecules from their predicted ECFP4 with a retrieval accuracy on theoretical
images of 95.4\%. Furthermore, this approach, unlike previous DL models,
assigns a confidence score, the Tanimoto similarity, to each of the candidate
molecules, thus providing information on the reliability of the identification.
  By construction, the number of times a certain substructure is present in the
molecule is lost during the hashing process, necessary to make them useful for
machine learning applications. We show that it is possible to complement the
fingerprint-based virtual screening with global information provided by another
DL model that predicts from the same HR--AFM stacks the chemical formula,
boosting the identification accuracy up to a 97.6\%. Finally, we perform a
limited test with experimental images, obtaining promising results towards the
application of this pipeline under real conditions
