Reviewer response for version 1
Ekins et al. have presented a crisp and lucid manuscript on a very relevant topic. 
They have presented a methodology that implements machine learning techniques to learn from known active and inactive compounds (an ever increasing set, that will tend to provide improved results as time goes by), and score a larger set of compounds (MicroSource Spectrum set of 2320 compounds). 
The in silico methodology described here provides an excellent method to quickly screen known compounds for possible therapies (against Ebola in particular), and other viruses in general. 
Finally, they demonstrate ( in vitro ) the increased effectiveness of three compounds - the antiviral tilorone and the antimalarials quinacrine and pyronaridine - in comparison to the known active chloroquine (albeit at a higher cytotoxicity) in inhibiting viral infection of HeLa cells. 
Further, their efforts in ensuring open-access to such tools is commendable as the next pathogen caused humanitarian crisis looms in some nations.
The biggest open question is how good are the molecular descriptors, and how much of this is serendipitous. 
For example, I find it hard to believe that molecular weight and the number of rotatable bonds can be good predictors of drug-protein interactions (although I may be wrong). 
I have been investigating promiscuous ligand protein interactions for a couple of years on a molecular basis ( http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0032011 , http://f1000research.com/articles/2-286/v3 ). 
One interesting example (unpublished) is suramin used in the treatment of African sleeping sickness (African trypanosomiasis) and river blindness (onchocerciasis), infections caused by parasites. 
Suramin binds eight non-homologous proteins in the PDB database, through different parts of the molecule and in binding sites that share little similarity in residues involved. 
Also, the molecule (in addition to the protein) undergoes conformational changes, underscoring the difficulty of computational methods to model such interactions. 
In the face of such data, the m/c learning models appear too simplistic.
Also, it is not completely clear why the 23, 31 and 34th compound was chosen from the Table S5, which is ordered on column H (all three have amines, don't the previous ones have it?).
Some minor comments:
- It would be an interesting case study to evaluate how favipiravir (which I understand is yet to be clinically approved in the US, but approved in Japan) and BCX4430 would rank through the m/c learning methodology.
- It would be good to have a set of images, and the corresponding nuclei counts obtained from CellProfiler. 
Is there a way to quantify the color green as a measure of viral infection?
- AlogP as a molecular descriptor has not been explained (page 3).
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